diff --git a/Applications/KeywordSpotting/MXChip-SRNN/README.md b/Applications/KeywordSpotting/MXChip-SRNN/README.md
new file mode 100644
index 000000000..8ed11894e
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/README.md
@@ -0,0 +1,46 @@
+# S-RNN for Speech Command Detection
+
+Here we demonstrate how SRNN can be used to deploy a key-word spotting model on
+the [Azure IoT Dev-Kit](https://microsoft.github.io/azure-iot-developer-kit/)
+powered by the Coretex M4. The model provided is based on the [Speech Commands Dataset](https://ai.googleblog.com/2017/08/launching-speech-commands-dataset.html). It is trainined to recognise commands in the set: `[go, no, on, up, bed, cat, dog, off, one, six, two, yes]`. When no keyword is detected, the screen will print 'Noise'.
+
+Unit testing and benchmarking code that was used to develop this implementation of S-RNN is provided in the `tests` directory and can be used for debugging purposes.
+
+## Instructions for Deployment 
+
+1. Follow instructions [here](https://github.com/VSChina/devkit-mbedos5-getstarted) 
+to set-up the MXChip environment. Verify that the set-up is
+working properly by burning the `GettingStarted` example mentioned there. 
+2. Clone the EdgeML repository. Let this repository lie in `$EDGEML_HOME`.
+3. Change directory to `devkit-mbedos5-getstarted` cloned in step 1.
+   ```
+      cd devkit-mbedos5-getstarted/
+   ```
+3. Remove the provided `GetStarted` example and replace it with
+   `$EDGEML_HOME/Applications/KeywordSpotting/MXChip-SRNN/`
+   ```
+      rm -r GetStarted
+      cp -r $EDGEML_HOME/Applications/KeywordSpotting/MXChip-SRNN ./
+   ```
+4. Open `devkit-mbedos5-getstarted/.mbedignore` in your favourite text editor
+   and append the following lines:
+   ```
+      MXChip-SRNN/test/*
+   ```
+5. Copy the provided build profile file, `develop_custom.json` into the
+   `mbed-os` profiles folder:
+   ```
+      cp MXChip-SRNN/develop_custom.json mbed-os/tools/profiles/
+   ```
+6. Compile using:
+   ```
+      mbed compile --profile develop_custom
+   ```
+
+7. Upload to MXChip IoT DevKit:
+  - Connect the MXChip IoT DevKit with your machine via USB.
+  - You will find a removable USB Mass Storage disk named AZ3166.
+  - Copy the
+  `.\BUILD\AZ3166\GCC_ARM-DEVELOP_CUSTOM\devkit-mbedos5-getstarted.bin` into this disk.
+  - The device will reboot and run the application. 
+
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/WakeWord-MXChip.cpp b/Applications/KeywordSpotting/MXChip-SRNN/WakeWord-MXChip.cpp
new file mode 100644
index 000000000..af5353685
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/WakeWord-MXChip.cpp
@@ -0,0 +1,125 @@
+#include "src/lib/sfastrnn_pipeline/sfastrnnpipeline.h"
+#include <Arduino.h>
+#include <AudioClassV2.h>
+#include "src/models/model.h"
+#include "src/lib/utils/circularq.h"
+
+extern struct FastRNNParams fastrnnParams0;
+extern struct FastRNNParams fastrnnParams1;
+extern struct FCParams fcParams;
+extern void initFastRNN0();
+extern void initFastRNN1();
+extern void initFC();
+extern const char *labelInvArr[];
+// A circular q for voting
+#define VOTE_WIN_LEN 10
+#define VOTE_MAJORITY 5
+FIFOCircularQ votingQ;
+static int votingContainer[VOTE_WIN_LEN];
+static int votingFrequence[NUM_LABELS];
+
+// TODO: Explain this
+#define TRANSFER_BUFFER_MAX_LEN 128
+static AudioClass& Audio = AudioClass::getInstance();
+char readBuffer[AUDIO_CHUNK_SIZE];
+static int transfer_buffer_curr_len = 0;
+static int16_t transfer_buffer[TRANSFER_BUFFER_MAX_LEN];
+
+void recordCallback(void) {
+    int length = Audio.readFromRecordBuffer(readBuffer, AUDIO_CHUNK_SIZE);
+    // We are 16bit (short) and not 8bit (char). Hence actual number of samples
+    // is half. Further, we need to ignore the second channel in the
+    // audio (interleaved with the first channel).
+    length = length / 2;
+    length = length - (length % 2);
+    length = length / 2;
+    if(length > TRANSFER_BUFFER_MAX_LEN)
+        error("Transfer buffer too small");
+    // Convert to 16 bit samples
+    int16_t *tempAudio = (int16_t*)readBuffer;
+    if (transfer_buffer_curr_len != 0) {
+        Serial.printf("Error: Transfer buffer not empty. %d dropped\n", length);
+        return;
+    }
+    // Drop every other sample (the second channel) while copying
+    for(int i = 0; i < length; i++)
+        transfer_buffer[i] = tempAudio[2 * i];
+    transfer_buffer_curr_len = length;
+}
+
+void init_record(){
+    // Sampling rate 16000Hz @ 16 bit resolution 
+    // This is hardcoded in the code. Don't change.
+    Audio.format(16000U, 16U);
+}
+
+void start_record(){
+  Audio.startRecord(recordCallback);
+}
+
+void prediction_callback(float *vec, int len){
+    int arg = argmax(vec, len);
+    int oldarg = *(int*)q_oldest(&votingQ);
+    if (oldarg >= NUM_LABELS || oldarg < 0)
+        oldarg = 0;
+    votingFrequence[arg]++;
+    votingFrequence[oldarg]--;
+    q_force_enqueue(&votingQ, &arg);
+    if (votingFrequence[arg] >= VOTE_MAJORITY){
+        char str[20];
+        sprintf(str, "Pred: %s (%d)", labelInvArr[arg], arg);
+        Screen.print(str, false);
+    }
+}
+
+
+void setup(){
+    q_init(&votingQ, votingContainer, VOTE_WIN_LEN, cb_write_int, cb_read_int);
+    votingFrequence[0] = 5;
+    Serial.begin(115200);
+    Screen.init();
+    delay(500);
+    initFastRNN0();
+    initFastRNN1();
+    initFC();
+    delay(500);
+    Screen.clean();
+    unsigned ret = sfastrnn2p_init(&fastrnnParams0,
+        &fastrnnParams1, &fcParams, prediction_callback);
+    Serial.printf("Return code: %d (init)\n", ret);
+    if(ret != 0)
+        error("Shallow FastRNN initialization failed (code %d)", ret);
+    if(ret != 0) while(1);
+    init_record();
+    delay(500);
+    Serial.println();
+    Serial.println("Ready");
+    Screen.print(0, "Ready");
+    delay(500);
+    start_record();
+}
+
+void loop(){
+    while (1){
+        if (transfer_buffer_curr_len == 0){
+            // For a 16, 16 fastRNN model, this can be pushed
+            // 6ms without causing errors.
+            rtos:wait_ms(5);
+            continue;
+        }
+        unsigned ret = sfastrnn2p_add_new_samples(transfer_buffer,
+            transfer_buffer_curr_len);
+        if(ret != 0){
+            Serial.printf("Error pushing to interface %d\n", ret);
+        }
+        static int count = 0;
+        count += transfer_buffer_curr_len;
+        if(count % (128 * 1000) == 0)
+            Serial.printf("Pushed %d seconds\n", (count/16000));
+        transfer_buffer_curr_len = 0;
+    }
+}
+
+void printStr(char *a){
+    Serial.println(a);
+}
\ No newline at end of file
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/develop_custom.json b/Applications/KeywordSpotting/MXChip-SRNN/develop_custom.json
new file mode 100644
index 000000000..b427d5474
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/develop_custom.json
@@ -0,0 +1,48 @@
+{
+    "GCC_ARM": {
+        "common": ["-c", "-Wall", "-Wextra",
+                   "-Wno-unused-parameter", "-Wno-missing-field-initializers",
+                   "-fmessage-length=0", "-fno-exceptions", "-fno-builtin",
+                   "-ffunction-sections", "-fdata-sections", "-funsigned-char",
+                   "-MMD", "-fno-delete-null-pointer-checks",
+                   "-fomit-frame-pointer", "-Os", "-mfloat-abi=hard", "-mfpu=fpv4-sp-d16",
+                   "-mcpu=cortex-m4", "-mfloat-abi=softfp", "-mfpu=fpv4-sp-d16", "-Lstatic",
+                   "-DDEBUG_MODE", "-DNFFT_512",  "-DFFT_F32", "-DNORMALIZE_FEAT"],
+        "asm": ["-x", "assembler-with-cpp"],
+        "c": ["-std=gnu99"],
+        "cxx": ["-std=gnu++11", "-fno-rtti", "-Wvla"],
+        "ld": ["-Wl,--gc-sections", "-Wl,--wrap,main", "-Wl,--wrap,_malloc_r",
+               "-Wl,--wrap,_free_r", "-Wl,--wrap,_realloc_r",
+               "-Wl,--wrap,_calloc_r", "-Wl,--wrap,exit", "-Wl,--wrap,atexit",
+               "-Wl,-n",
+               "-u _printf_float"]
+    },
+    "ARM": {
+        "common": ["-c", "--gnu", "-Otime", "--split_sections",
+                   "--apcs=interwork", "--brief_diagnostics", "--restrict",
+                   "--multibyte_chars", "-O3"],
+        "asm": [],
+        "c": ["--md", "--no_depend_system_headers", "--c99", "-D__ASSERT_MSG"],
+        "cxx": ["--cpp", "--no_rtti", "--no_vla"],
+        "ld": []
+    },
+    "uARM": {
+        "common": ["-c", "--gnu", "-Otime", "--split_sections",
+                   "--apcs=interwork", "--brief_diagnostics", "--restrict",
+                   "--multibyte_chars", "-O3", "-D__MICROLIB",
+                   "--library_type=microlib", "-DMBED_RTOS_SINGLE_THREAD"],
+        "asm": [],
+        "c": ["--md", "--no_depend_system_headers", "--c99", "-D__ASSERT_MSG"],
+        "cxx": ["--cpp", "--no_rtti", "--no_vla"],
+        "ld": ["--library_type=microlib"]
+    },
+    "IAR": {
+        "common": [
+            "--no_wrap_diagnostics", "-e",
+            "--diag_suppress=Pa050,Pa084,Pa093,Pa082", "-Oh"],
+        "asm": [],
+        "c": ["--vla"],
+        "cxx": ["--guard_calls", "--no_static_destruction"],
+        "ld": ["--skip_dynamic_initialization", "--threaded_lib"]
+    }
+}
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/debug_mode/debugmethods.h b/Applications/KeywordSpotting/MXChip-SRNN/src/debug_mode/debugmethods.h
new file mode 100644
index 000000000..d084ed0ac
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/src/debug_mode/debugmethods.h
@@ -0,0 +1,18 @@
+#include <arm_math.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+void printFloatArrF32(float32_t *, int, float);
+void printFloatArrQ31(q31_t *, int, float);
+void printIntArr(int32_t *, int, int);
+void printHexQ31(q31_t);
+void printInt32(int32_t);
+void printVoid(void *);
+void printStr(char *);
+void printFloatAddr(float *);
+
+#ifdef __cplusplus
+}
+#endif
\ No newline at end of file
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/fastrnn.c b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/fastrnn.c
new file mode 100644
index 000000000..187675ceb
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/fastrnn.c
@@ -0,0 +1,43 @@
+#include "fastrnn.h"
+
+void combineHX(const struct FastRNNParams *fastrnnParams, const float *h,
+        const float *x, float *dst){
+    memcpy(dst, h, fastrnnParams->statesLen * sizeof(float));
+    memcpy(&(dst[fastrnnParams->statesLen]), x, fastrnnParams->featLen* sizeof(float));
+}
+
+void FastRNNStep(const struct FastRNNParams *fastrnnParams, const float *x,
+        const float *input_h, float *result_h){
+    unsigned statesLen = fastrnnParams->statesLen;
+    unsigned featLen = fastrnnParams->featLen;
+    float h[statesLen];
+    memcpy(h, input_h, statesLen * sizeof(float));
+    float combinedOut[statesLen];
+    float hx[statesLen + featLen];
+    combineHX(fastrnnParams, h, x, hx);
+    // W[h, x]
+    matrixVectorMul(fastrnnParams->W, statesLen, statesLen + featLen, hx,
+            combinedOut);
+    // h_ = h_ + b 
+    vectorVectorAdd(combinedOut, fastrnnParams->b, statesLen);
+    // Apply non-linearity (currently only sigmoid)
+    vsigmoid(combinedOut, statesLen);
+    scalarVectorMul(combinedOut, statesLen, fastrnnParams->alpha);
+    scalarVectorMul(h, statesLen, fastrnnParams->beta);
+    vectorVectorAdd(combinedOut, h, statesLen);
+    memcpy(result_h, combinedOut, statesLen * sizeof(float));
+}
+
+
+void FastRNNInference(const struct FastRNNParams *fastrnnParams,
+        const float x[], float* result_h){
+    for(int i = 0; i < fastrnnParams->statesLen; i++){
+        result_h[i] = 0;
+    }
+    for (int t = 0; t < fastrnnParams->timeSteps; t++){
+        FastRNNStep(fastrnnParams, (float*)&(x[t * fastrnnParams->featLen]),
+                result_h, result_h);
+    }
+}
+
+
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/fastrnn.h b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/fastrnn.h
new file mode 100644
index 000000000..39fddf722
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/fastrnn.h
@@ -0,0 +1,39 @@
+#pragma once
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#include <math.h>
+#include "../utils/helpermath.h"
+#include <string.h>
+
+struct FastRNNParams {
+    // h~ = W1.h + W2.x + b
+    // The projection matrix W1 and W2 concatenated along axis=1, [W1, W2]
+    // in row major order. Will be of dimension [n_hid, (n_hid + n_inp)] in
+    // numpy.
+    float* W;
+    // The bias vector. Of dimension [n_hidden]
+    float *b;
+    // Alpha and beta for FastRNN (sigmoided version)
+    float alpha; float beta;
+    unsigned timeSteps;
+    unsigned featLen;
+    unsigned statesLen;
+};
+
+// FastRNNParams: Pointer to an instance of FastRNNParams
+// x: Input data. Should be of shape [numtime_steps, num_feats] flattened to
+//    1-D. That is, the ith time step will be the VECTOR x[i * num_feats]
+// result_h: hidden-state(h) stored in this vector.
+void FastRNNInference(const struct FastRNNParams *fastrnnParams, const float x[],
+        float *result_h);
+void FastRNNStep(const struct FastRNNParams *fastrnnParams, const float *x,
+        const float *input_h, float *result_h);
+void combineHX(const struct FastRNNParams *fastrnnParams, const float *h,
+        const float *x, float *dst);
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/fc.c b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/fc.c
new file mode 100644
index 000000000..b60f0b5c4
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/fc.c
@@ -0,0 +1,9 @@
+#include "fc.h"
+
+void FCInference(const struct FCParams* fcParams, const float x[],
+	float* result, unsigned nonLinearity){
+	matrixVectorMul(fcParams->W, fcParams->outputDim,
+		fcParams->inputDim, x, result);
+	vectorVectorAdd(result, fcParams->B, fcParams->outputDim);
+}
+
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/fc.h b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/fc.h
new file mode 100644
index 000000000..f23c6da9c
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/fc.h
@@ -0,0 +1,39 @@
+#pragma once
+
+#ifdef __cplusplus
+extern "C" {
+#endif 
+
+#include <math.h>
+#include "../utils/helpermath.h"
+
+/* An instance of FCParams needs to be defined else where. This will hold the
+ * model matrices. You pass a pointer to this FCParams instance here to perform
+ * your computations.
+ */
+
+struct FCParams{
+	// let x in a vector of size n = input dim
+	// And let the hidden dim (or output dim ) be m
+	// Then FC would do softmax(Wx) were W is m x n
+	float *W;
+	float *B;
+	unsigned inputDim;
+	unsigned outputDim;
+};
+
+//
+// Set nonLinearity can be used to perform non-linearity on outputs. This
+// feature is not implemented as of now and the raw outputs are returned.
+// 
+// params: The FCParams struct instance containing the parameters for the
+//		current layer.
+//	x: Input data 
+//	result: Float array to hold the FC result.
+//	nonLinearity: Choice of non-linearity to use. Currently not implemented.
+void FCInference(const struct FCParams* params, const float x[], float* result, 
+		unsigned nonLinearity);
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/lstm.c b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/lstm.c
new file mode 100644
index 000000000..6c9fd8700
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/lstm.c
@@ -0,0 +1,70 @@
+#include "lstm.h"
+
+void combineXH(const struct LSTMParams *lstmParams, const float *x,
+		const float *h, float *dst){
+	// TODO: Use memcpy to make this faster
+	memcpy(dst, x, lstmParams->featLen * sizeof(float));
+	memcpy(&(dst[lstmParams->featLen]), h, lstmParams->statesLen * sizeof(float));
+}
+
+void LSTMStep(const struct LSTMParams *lstmParams, const float *x,
+		const float *input_c_h, float *result_c_h_o){
+	unsigned statesLen = lstmParams->statesLen;
+
+	float c[statesLen];
+	float h[statesLen];
+	float o[statesLen];
+	memcpy(c, &input_c_h[0*statesLen], statesLen * sizeof(float));
+	memcpy(h, &input_c_h[1*statesLen], statesLen * sizeof(float));
+
+	float combinedOut[4 * (lstmParams->statesLen)];
+	float xh[lstmParams->statesLen + lstmParams->featLen];
+	combineXH(lstmParams, x, h, xh);
+	matrixVectorMul(lstmParams->W, 4*lstmParams->statesLen,
+		lstmParams->statesLen + lstmParams->featLen,
+		xh, combinedOut);
+	vectorVectorAdd(combinedOut, lstmParams->B,
+		4 * lstmParams->statesLen);
+	// Apply non-linearity
+	// i_t
+	vsigmoid(&combinedOut[0*lstmParams->statesLen], lstmParams->statesLen);
+	// c_cap_t
+	vtanh(&combinedOut[1*lstmParams->statesLen], lstmParams->statesLen);
+	// f_t (after adding forget bias)
+	for(int i = 0; i < lstmParams->statesLen; i++)
+		combinedOut[2*lstmParams->statesLen + i] += lstmParams->forgetBias;
+	vsigmoid(&combinedOut[2*lstmParams->statesLen], lstmParams->statesLen);
+	// o_t
+	vsigmoid(&combinedOut[3*lstmParams->statesLen], lstmParams->statesLen);
+	
+	// update c
+	for(int i = 0; i < lstmParams->statesLen; i++){
+		//c_t = (f_t + forget_bias)*C_t-1 + i_t*c_cap_t
+		c[i] = combinedOut[2*lstmParams->statesLen + i] * c[i];
+		c[i] += combinedOut[0*lstmParams->statesLen +
+			i]*combinedOut[1*lstmParams->statesLen + i];
+		//o_t
+		o[i] = combinedOut[3*lstmParams->statesLen + i];
+		//h_t
+		h[i] = o[i] * tanh(c[i]);
+	}
+	// returns c, h, o
+	for(int i = 0; i < lstmParams->statesLen; i++){
+		result_c_h_o[lstmParams->statesLen * 0 + i] = c[i];
+		result_c_h_o[lstmParams->statesLen * 1 + i] = h[i];
+		result_c_h_o[lstmParams->statesLen * 2 + i] = o[i];
+	}
+}
+
+
+void LSTMInference(const struct LSTMParams *lstmParams, const float x[],
+		float* result_c_h_o){
+	for(int i = 0; i < 3 * lstmParams->statesLen; i++){
+		result_c_h_o[i] = 0;
+	}
+	for (int t = 0; t < lstmParams->timeSteps; t++){
+		LSTMStep(lstmParams, (float*)&(x[t * lstmParams->featLen]), result_c_h_o, result_c_h_o);
+	}
+}
+
+
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/lstm.h b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/lstm.h
new file mode 100644
index 000000000..f5e122068
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/lstm.h
@@ -0,0 +1,33 @@
+#pragma once
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#include <math.h>
+#include "../utils/helpermath.h"
+
+struct LSTMParams {
+	float* B;
+	unsigned timeSteps;
+	unsigned featLen;
+	unsigned statesLen;
+	float forgetBias;
+	float *W; //  [W_i, W_c, W_f, W_o]
+};
+
+// lstmParams: Pointer to an instance of LSTMParams.
+// x: Input data. Should be of shape [numtime_steps, num_feats] flattened to
+// 1-D. That is, the ith time step will be the VECTOR x[i * num_feats]
+// result_c_h_o: The cell-state (c), hidden-state(h) and output (o) will be
+// stored in this vector.
+void LSTMInference(const struct LSTMParams *lstmParams, const float x[],
+		float *result_c_h_o);
+void LSTMStep(const struct LSTMParams *lstmParams, const float *x,
+		const float *input_c_h, float *result_c_h_o);
+void combineXH(const struct LSTMParams *lstmParams, const float *x,
+		const float *h, float *dst);
+
+#ifdef __cplusplus
+}
+#endif
\ No newline at end of file
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/sfastrnn.c b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/sfastrnn.c
new file mode 100644
index 000000000..61feff383
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/sfastrnn.c
@@ -0,0 +1,33 @@
+#include "sfastrnn.h"
+
+unsigned initSFastRNN2(struct SFastRNNParams2 *mis2,
+        struct FastRNNParams *p0, struct FastRNNParams *p1,
+        float *container1, float *h0_buffer, float *inp1_buffer){
+    mis2->frnn0 = p0;
+    mis2->frnn1 = p1;
+    mis2->timeSteps0 = p0->timeSteps;
+    mis2->timeSteps1 = p1->timeSteps;
+    mis2->__featLen0 = p0->featLen;
+    mis2->__featLen1 = p0->statesLen;
+    if (mis2->__featLen1 != p1->featLen)
+        return 1;
+    int size = p1->timeSteps * p0->statesLen;
+    q_init(&mis2->h0q, container1, size,
+            cb_write_float, cb_read_float);
+    mis2->h0_buffer = h0_buffer;
+    mis2->inp1_buffer = inp1_buffer;
+    return 0;
+}
+
+void SFastRNNInference2(struct SFastRNNParams2 *params, const float *x,
+        float *result_h) {
+    unsigned statesLen0 = params->frnn0->statesLen;
+    unsigned timeSteps1 = params->frnn1->timeSteps;
+    float *h0 = params->h0_buffer;
+    float *inp1 = params->inp1_buffer;
+    FastRNNInference(params->frnn0, x, h0);
+    q_force_enqueue_batch(&(params->h0q), h0, sizeof(float), statesLen0);
+    q_flatten_float(&(params->h0q), inp1);
+    FastRNNInference(params->frnn1, inp1, result_h);
+}
+
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/sfastrnn.h b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/sfastrnn.h
new file mode 100644
index 000000000..3a12d6138
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/algorithms/sfastrnn.h
@@ -0,0 +1,67 @@
+/*
+ * Shallow FastRNN
+ * ---------------
+ * Currently only a 2 layer network is supported through
+ * in the current format, implementing 3 layer is straight
+ * forward.
+ */
+
+#pragma once
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#include "../utils/helpermath.h"
+#include "../algorithms/fastrnn.h"
+#include "../utils/circularq.h"
+
+
+// 2 Layer S-FastRNN
+struct SFastRNNParams2 {
+    struct FastRNNParams *frnn0;
+    struct FastRNNParams *frnn1;
+    unsigned timeSteps0;
+    unsigned timeSteps1;
+    // These parameters are set internally
+    unsigned __featLen0;
+    unsigned __featLen1;
+    // Container for hidden states. Should be of shape
+    // [featLen1 * timeSteps1]
+    FIFOCircularQ h0q;
+    // We need some buffer space to run. I don't want to
+    // define these on the stack as that has often caused
+    // problems with mbed-os. Don't worry about what these
+    // are. h0_buffer has to be statesLen0 long and
+    // inp1_buffer has to be statesLen0 * timeSteps1 long.
+    float *h0_buffer;
+    float *inp1_buffer;
+};
+
+// 2 Layer S-FastRNN initialization
+// sParams2: An instance of SFastRNNInference2 to be initialized.
+// p0: The FastRNNParams for level 0
+// p1: The FastRNNParams for level 1
+// h0container: A float array of size [numTimeSteps1 * hiddenStates0]
+//              To store the intermediate hidden states.
+// h0_buffer  :
+// inp1_buffer: We need some buffer space to run. I don't want to
+//       define these on the stack as that has often caused
+//       problems with mbed-os. Don't worry about what these
+//       are. h0_buffer has to be statesLen0 long and
+//       inp1_buffer has to be statesLen0 * timeSteps1 long.
+unsigned initSFastRNN2(struct SFastRNNParams2 *sParams,
+        struct FastRNNParams *p0, struct FastRNNParams *p1,
+        float *h0container, float *h0_buffer, float *h1_buffer);
+
+// sParams : An instance of SFastRNNInference2 with the parameters
+//      initialized through initSFastRNN2.
+// x : Input vector of shape [featLen1 * timeSteps1]
+// result_h : result hidden state.
+void SFastRNNInference2(struct SFastRNNParams2 *sParams, const float *x,
+        float *result_h);
+
+#ifdef __cplusplus
+}
+#endif
+
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/lib/debug_mode/debugmethods.h b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/debug_mode/debugmethods.h
new file mode 100644
index 000000000..d084ed0ac
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/debug_mode/debugmethods.h
@@ -0,0 +1,18 @@
+#include <arm_math.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+void printFloatArrF32(float32_t *, int, float);
+void printFloatArrQ31(q31_t *, int, float);
+void printIntArr(int32_t *, int, int);
+void printHexQ31(q31_t);
+void printInt32(int32_t);
+void printVoid(void *);
+void printStr(char *);
+void printFloatAddr(float *);
+
+#ifdef __cplusplus
+}
+#endif
\ No newline at end of file
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/lib/featurizer/logfbank.c b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/featurizer/logfbank.c
new file mode 100644
index 000000000..7ef545a27
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/featurizer/logfbank.c
@@ -0,0 +1,244 @@
+#include "logfbank.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+float32_t meltohz(float32_t mel);
+float32_t hztomel(float32_t hz);
+
+#ifdef FFT_Q31
+    static q31_t temp_in_q31[2 * NFFT];
+    static q31_t powspectrum_q31[(NFFT/ 2 + 1)];
+    static q31_t feat_q31[NFILT];
+#elif FFT_F32
+    static float32_t temp_in[2 * NFFT];
+    static float32_t powspectrum[(NFFT/ 2 + 1)];
+    static float32_t feat[NFILT];
+#endif
+    static void preemph_q31(q31_t *temp_in, q31_t preemph,
+        int len, q31_t tail);
+    static void preemph_f32(float32_t *temp_in, float32_t preemph,
+        int len, float tail);
+    static void next_pow_2_uint32(uint32_t n, uint32_t *pow2, uint8_t *index);
+    static void int32_to_q31(const int32_t *srcVec,  q31_t *destVec,
+        int32_t len, uint8_t max_shift); 
+
+#ifdef __cplusplus
+}
+#endif
+
+
+void logfbank(
+    float32_t *mfcc_result,
+    const int32_t *data,
+    const void *vfbank,
+    const int32_t preemph_tail
+    ){
+    int ifftFlag = 0, doBitReverse = DO_BIT_REVERSE;
+
+#ifdef FFT_Q31
+    uint32_t scaleVal; uint8_t scaleShift;
+    int32_t max = 0, min = 0;
+    for(int i = 0; i < NFFT; i++){
+        int32_t val = data[i];
+        if(val > max) max = val;
+        if(val < min) min = val;
+    }
+    max = (max > -1 * min) ? max: -1 * min;
+    max = (max > fabs(preemph_tail)) ? max: fabs(preemph_tail);
+    next_pow_2_uint32((uint32_t) max, &scaleVal, &scaleShift);
+    scaleVal *= 2; scaleShift += 1;
+    int32_to_q31(data, temp_in_q31, NFFT, scaleShift);
+    // windowing q31 
+    q31_t tail, preemph;
+    float pre_f = (float)PREEMPH;
+    float tail_f = (float)preemph_tail / scaleVal;
+    arm_float_to_q31(&tail_f, &tail, 1);
+    arm_float_to_q31(&pre_f, &preemph, 1);
+    preemph_q31(temp_in_q31, preemph, (int)FRAME_LEN, tail);
+    for(int i = 0; i < NFFT; i++){
+        q31_t val = (temp_in_q31[NFFT - 1 - i] >> (NFFT_SHIFT));
+        temp_in_q31[2 * NFFT - 2 - 2 * i] = val;
+        temp_in_q31[2 * NFFT - 1 - 2 * i] = 0;
+    }
+    arm_cfft_q31(&arm_cfft_sR_q31_len512, temp_in_q31, ifftFlag, doBitReverse);
+    // Scale back otherwise we will underflow when computing magnitude
+    // We scale by NFFT_SHIT only. Scaling up too much causes saturation.
+    arm_scale_q31(temp_in_q31, 0x7fffffff, NFFT_SHIFT, temp_in_q31, 2 * NFFT);
+    arm_cmplx_mag_squared_q31(temp_in_q31, powspectrum_q31, NFFT);
+    // We are now in q29. Need to scale by 4 (shift by 2) to get back to q31
+    arm_scale_q31(powspectrum_q31, 0x7fffffff, 2, powspectrum_q31, (NFFT/2+1));
+    // Trying the matmul version. Scaling a little bit more so that
+    // we dont loose too much precision in matmuls
+    arm_scale_q31(powspectrum_q31, 0x7fffffff, NFFT_SHIFT,
+        powspectrum_q31, (NFFT/2 + 1));
+    arm_matrix_instance_q31 mfilters, mpowspectrum, mfeat;
+    mfilters.numRows = NFILT; mfilters.numCols = NFFT/2 + 1;
+    mfilters.pData = (q31_t *)vfbank;
+    mpowspectrum.numRows = NFFT/2 + 1; mpowspectrum.numCols = 1;
+    mpowspectrum.pData = powspectrum_q31;
+    mfeat.numRows = NFILT; mfeat.numCols = 1;
+    mfeat.pData = feat_q31;
+    arm_status status;
+    // We are sufficiently scaled down at this point;
+    // don't want to rescale.
+    status = arm_mat_mult_fast_q31(&mfilters, &mpowspectrum, &mfeat);
+    // if (status != ARM_MATH_SUCCESS); do something ?
+    arm_q31_to_float(mfeat.pData, mfcc_result, NFILT);
+    arm_scale_f32(mfcc_result, scaleVal * scaleVal, mfcc_result, NFILT);
+    for(int i = 0; i < NFILT; i++)
+        if (mfcc_result[i] > 0.0f) mfcc_result[i] = log(mfcc_result[i]);
+        else mfcc_result[i] = log(DBL_EPS);
+    return;
+#elif FFT_F32
+    // Fbank is in float32_t
+    const float32_t *fbank = (float32_t*) vfbank;
+    // Pre-emphasis
+    for(int i = 0; i < NFFT; i++){
+        temp_in[i] = (float)data[i];
+    }
+    preemph_f32(temp_in, (float32_t)PREEMPH, (int)FRAME_LEN, preemph_tail);
+    // Convert to complex notation
+    // memset(temp_in, 0, 2 * NFFT * sizeof(float32_t)); is slower
+    for(int i = 0; i < NFFT; i++){
+        temp_in[2 * NFFT - 2 - 2 * i] = temp_in[NFFT - 1 - i];
+        temp_in[2 * NFFT - 1 - 2 * i] = 0;
+    }
+    arm_cfft_f32(&arm_cfft_sR_f32_len512, temp_in, ifftFlag, doBitReverse);
+    arm_cmplx_mag_squared_f32(temp_in, powspectrum, NFFT);
+    scalarVectorMul(powspectrum, (NFFT / 2 + 1), (float32_t)(1.0 / NFFT));
+
+    // The below is the non-mat-mul version of 
+    // computing filter bank energies. This is faster than matmul
+    // version without FPU
+    // ---
+    // const float32_t *fbank2 = (float32_t*) vfbank;
+    // for (int l = 0; l < NFILT; l++) {
+    //     feat[l] = 0.0f;
+    //     int k = 0;
+    //     for (k = 0; k < NFFT / 2 + 1; k++)
+    //         feat[l] += powspectrum[k] * fbank2[l * (NFFT/ 2 + 1) + k];
+    //     if (feat[l] > 0.0f) feat[l] = log(feat[l]);
+    //     else feat[l] = log(DBL_EPS);
+    // }
+    // The below is the matmul-equivalent
+    // This is slightly slower with soft float ABI. Keeping it here
+    // since things out to be faster with SIMD and FPU.
+    // ---
+    arm_matrix_instance_f32 mfilters, mpowspectrum, mfeat;
+    mfilters.numRows = NFILT; mfilters.numCols = NFFT/2 + 1;
+    mfilters.pData = fbank;
+    mpowspectrum.numRows = NFFT/2 + 1; mpowspectrum.numCols = 1;
+    mpowspectrum.pData = powspectrum;
+    mfeat.numRows = NFILT; mfeat.numCols = 1;
+    mfeat.pData = feat;
+    arm_status status;
+    status = arm_mat_mult_f32(&mfilters, &mpowspectrum, &mfeat);
+    for(int i = 0; i < NFILT; i++)
+        if (feat[i] > 0.0f) feat[i] = log(feat[i]);
+        else feat[i] = log(DBL_EPS);
+    // --
+    arm_copy_f32(feat, mfcc_result, NFILT);
+    return;
+#endif
+}
+
+void get_filterbank_parameters(float32_t *fbank, int nfilt,
+    int samplingRate, int nfft){
+    float32_t lowmel = hztomel(0.0f);
+    float32_t highmel = hztomel(samplingRate / 2.0f);
+
+    // Generate nfilt center frequencies linearly spaced in the mel scale
+    float32_t bin[nfilt + 2];
+    int i = 0;
+    for (i = 0; i <= nfilt + 1; i++)
+        bin[i] = floor(meltohz(i * (highmel - lowmel) /
+            (nfilt + 1) + lowmel) * (nfft + 1) / samplingRate);
+
+    memset(fbank, 0, (nfft / 2 + 1) * nfilt * sizeof(float32_t));
+    for (i = 0; i < nfilt; i++) {
+        int j = 0;
+        for (j = (int)bin[i]; j < (int)bin[i + 1]; j++)
+            fbank[i * (nfft / 2 + 1) + j] = (j - bin[i]) / (bin[i + 1] - bin[i]);
+        for (j = (int)bin[i + 1]; j < (int)bin[i + 2]; j++)
+            fbank[i * (nfft / 2 + 1) + j] = (bin[i + 2] - j) / (bin[i + 2] - bin[i + 1]);
+    }
+}
+
+float32_t hztomel(float32_t hz) {
+    return 2595 * log10(1 + hz / 700.0f);
+}
+
+float32_t meltohz(float32_t mel){
+    return 700 * (pow(10, mel / 2595.0f) - 1);
+}
+
+void preemph_q31(q31_t *temp_in, q31_t preemph, int len, q31_t tail){
+    int lim;
+    int remaining = len;
+    static q31_t subs[10], batch = 10;
+    int start = 0;
+    while (remaining > 0){
+        lim = (remaining < batch ? remaining: batch);
+        subs[0] = tail;
+        tail = temp_in[start + lim - 1];
+        for(int i = 1 ; i < lim; i++)
+            subs[i] = temp_in[start + i - 1];
+        arm_scale_q31(subs, preemph, 0, subs, lim);
+        // printFloatArrQ31(&temp_in[start], 10, 512);
+        // printFloatArrQ31(subs, 10, 512);
+        arm_sub_q31(&temp_in[start], subs, &temp_in[start], lim);
+        // printFloatArrQ31(&temp_in[start], 10, 512);
+        // printInt32(0);
+        start += lim;
+        remaining -= lim;
+    }
+}
+
+void preemph_f32(float32_t *temp_in, float32_t preemph,
+    int len, float tail){
+    for(int i = len-1; i > 0; i--){
+        temp_in[i] = temp_in[i] - preemph * temp_in[i-1];
+    }
+    temp_in[0] = temp_in[0] - preemph * tail;
+}
+
+void next_pow_2_uint32(uint32_t n, uint32_t *pow2, uint8_t *index) { 
+    // Convert N to power of 2
+    n--; 
+    n |= n >> 1; 
+    n |= n >> 2; 
+    n |= n >> 4; 
+    n |= n >> 8; 
+    n |= n >> 16; 
+    n++;
+    *pow2 = n;
+    // Do a fast log to find which index this is (shift amount)
+    // WARNING: 0 means no shift (if n==1 or n==0)
+    if (n == 0) {*index = 0; return;}
+    if ((n & 1) == 1) {*index = 0; return;}
+    for(uint8_t i = 1; i <= 31; i++)
+        if ((n >> i) == 1) {*index = i; return;}
+} 
+
+// Converting int32 to q31 requires reversing the order.
+// For instance, the number 17 in int is 0x00000011 but this
+// can't be represented in q31. We need atleast q8.24 for this.
+// In q8.24, we the representation is 0x11.000000 .
+//
+// Hence we implement this conversion as follows - we copy 'max_shift'
+// number of  bits from the left side of the int and into the
+// first 'max_shift'  bits of a zero-fileld 32 sized bitvector (for
+// lack of better terminology). Hence, the returned number in q31 is the
+// original number scaled down by 2^(max_shift).
+//
+// max_shift should at least be 2 and atmost 31 for the behaviour
+// to be defined.
+void int32_to_q31(const int32_t *srcVec,  q31_t *destVec,
+    int32_t len, uint8_t max_shift){
+    // Create a mask of max_shift
+    for(int i = 0; i < len; i++){
+        destVec[i] = srcVec[i] << (31 - (max_shift));
+    }
+}
\ No newline at end of file
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/lib/featurizer/logfbank.h b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/featurizer/logfbank.h
new file mode 100644
index 000000000..e412833e7
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/featurizer/logfbank.h
@@ -0,0 +1,76 @@
+#pragma once
+#include <arm_math.h>
+#include <arm_const_structs.h>
+#include <string.h>
+#include "../utils/helpermath.h"
+
+#ifdef DEBUG_MODE
+    #include "../debug_mode/debugmethods.h"
+#endif
+
+#define DBL_EPS 2.2204460492503131e-16
+// #define PI 3.14159265358979323846264338327
+#define SAMPLING_RATE 16000
+#define FRAME_LEN 400
+#define STRIDE 160
+#define NFILT 32
+#define HAMMING 2
+#define DO_BIT_REVERSE 1
+
+// Various supported windowing schemes
+#define WIN_RECTANGULAR 0
+#define WIN_HANNING 1
+#define WIN_HAMMING 2
+#define PREEMPH 0.97
+
+#ifdef __cplusplus
+extern "C" {
+#endif 
+
+#if defined(NFFT_512)
+    #define NFFT 512
+    #define NFFT_SHIFT 9
+#endif
+#if defined(FFT_Q31)
+    #define CFFT_INSTANCE arm_cfft_sR_q31_len512
+    #define CFFT_FUNC arm_cfft_q31
+#elif  defined(FFT_F32)
+    #define CFFT_INSTANCE arm_cfft_sR_f32_len512
+    #define CFFT_FUNC arm_cfft_f32
+#endif
+//
+// Computes the MFCC of one frame (WINLEN) of data. Note that windowing
+// is not performed as part of this function. It needs to be done outside this
+// method before data is passed on. Also note that many MFCC parameters like
+// number of filters, stride, sampling rate, fft length etc are fixed as
+// as compile time constants. Hence, this method is not a generic 
+// implementation - like the one found in the Pi3 or Pi0 implementations.
+//
+// mfcc_result 		- stores the output of the MFCC computation.
+// data 			- Data of length WINLEN
+// fbank            - The filter bank for this configuration. Can be obtained by
+//                    get_filterbank_parameters_xx
+// preemph_tail     - Tail element of the previous window for preemphasis.
+//                    Set to 0 if this is the first window.
+
+void logfbank(
+    float32_t *mfcc_result,
+    const int32_t *data,
+    // The fbank filters. These are calculated
+    // externally and passed in the interest of
+    // speed. Use get_filterbank_parameters for this.
+    const void *vfbank,
+    const int32_t preemph_tail
+);
+
+void get_filterbank_parameters(
+    float32_t *fbank,
+    int nfilt,
+    int samplingRate,
+    int nfft
+);
+
+#ifdef __cplusplus
+}
+#endif // __cplusplus
+
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/lib/sfastrnn_pipeline/sfastrnnpipeline.cpp b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/sfastrnn_pipeline/sfastrnnpipeline.cpp
new file mode 100644
index 000000000..cce8194c8
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/sfastrnn_pipeline/sfastrnnpipeline.cpp
@@ -0,0 +1,184 @@
+#include "../sfastrnn_pipeline/sfastrnnpipeline.h"
+
+static unsigned init_error_code = SFASTRNN2P_UNINITIALIZED;
+// Will be used as the buffer to hold the layer 0 input.
+// That is, the feature vector
+static float feat_container[FEAT_BUFFER_LEN];
+static float flat_feat_vec[FEAT_BUFFER_LEN];
+static float mfcc_result[NFILT];
+FIFOCircularQ featVecQ;
+// Will be used as the buffer to hold the layer 1 input.
+// That is, the layer 0 hidden states.
+static float h0_container[H0_BUFFER_LEN];
+// Will host the SFastRNNParams for 2 layer network
+static SFastRNNParams2 sfastrnn2_params;
+static float h0_buffer[H0_LEN];
+static float inp1_buffer[H0_BUFFER_LEN];
+// Will host the output states of SFastRNN2
+static float final_h[FINAL_H_LEN];
+// Will host the FC layer and its output from the FC layer
+struct FCParams *fcparams;
+static float logits[NUM_LABELS];
+// A queue, its container and mutex to hold audio samples.
+FIFOCircularQ audioQ;
+static int16_t audio_samples_container[AUDIO_SAMPLES_BUFFER_LEN];
+rtos::Mutex audio_buffer_mutex;
+// Prediction function for a separate thread
+rtos::Thread pred_thread;
+void pred_func();
+// One frame of audio
+static int32_t audio_frame_buffer[NFFT];
+static int8_t quit_prediction_flag;
+// We need to keep track of the tail element
+// of each audio frame to make sure pre-emphasis
+// is correctly performed
+static int16_t preemph_tail;
+// For F-bank filters
+static float32_t fbank_f32[NFILT * (NFFT/ 2 + 1)];
+#ifdef FFT_Q31
+    static q31_t fbank_q31[NFILT * (NFFT/ 2 + 1)];
+#endif
+static void (*prediction_cb)(float *, int);
+
+
+unsigned sfastrnn2p_init(struct FastRNNParams *p0,
+    struct FastRNNParams *p1, struct FCParams *fc,
+    void (*pred_cb)(float*, int)){
+    if(init_error_code != SFASTRNN2P_UNINITIALIZED)
+        return SFASTRNN2P_MULTIPLE_INIT_ERROR;
+
+    unsigned ret = 0;
+    ret = initSFastRNN2(&sfastrnn2_params, p0, p1, h0_container,
+        h0_buffer, inp1_buffer);
+    // Happens if input and output dimensions mismatch
+    if(ret != 0)
+        return SFASTRNN2P_SFASTRNN2_INIT_ERR;
+    int size = p0->statesLen * p1->timeSteps;
+    if (size != H0_BUFFER_LEN)
+        return SFASTRNN2P_H0_BUFFER_LEN_ERR;
+    // Check if final_h has correct dimensions
+    if (FINAL_H_LEN != p1->statesLen)
+        return SFASTRNN2P_FINAL_H_LEN_ERR;
+    if (H0_LEN != p0->statesLen)
+        return SFASTRNN2P_H0_LEN_ERR;
+    // Initialize the FC layer and check for output len correctness
+    fcparams = fc;
+    if (NUM_LABELS != fcparams->outputDim)
+        return SFASTRNN2P_FC_OUT_LEN_ERR;
+    if (FINAL_H_LEN != fcparams->inputDim)
+        return SFASTRNN2P_FC_IN_LEN_ERR;
+    // Initialize the audio queue 
+    q_init(&audioQ, audio_samples_container, AUDIO_SAMPLES_BUFFER_LEN,
+        cb_write_int16, cb_read_int16);
+    // Initialize the feature vector buffer
+    size = p0->timeSteps * p0->featLen;
+    if (size != FEAT_BUFFER_LEN)
+        return SFASTRNN2P_FEAT_BUFFER_LEN_ERR;
+    q_init(&featVecQ, feat_container, FEAT_BUFFER_LEN,
+        cb_write_float, cb_read_float);
+    // Initialize fbanks
+    get_filterbank_parameters(fbank_f32, NFILT, SAMPLING_RATE, NFFT);
+    #ifdef FFT_Q31
+        arm_float_to_q31(fbank_f32, fbank_q31, NFILT * (NFFT/2 + 1));
+    #endif
+    // Initialize the frame buffer
+    if(NFFT < FRAME_LEN)
+        return SFASTRNN2P_NFFT_TOO_SMALL_ERR;
+    memset(audio_frame_buffer, 0, NFFT * sizeof(int32_t));
+    // If we these methods don't function, we don't return.
+    // Also, we are royally screwed.
+    audio_buffer_mutex.lock();
+    audio_buffer_mutex.unlock();
+    // Start the prediction thread at default priority
+    // To pass arguments, use the callback API in RTOS
+    osStatus status = pred_thread.start(pred_func);
+    if (status != osOK)
+        return SFASTRNN2P_PRED_THR_SPWAN_ERR;
+    quit_prediction_flag = 0;
+    preemph_tail = 0;
+    // Set the prediction call_back
+    prediction_cb = pred_cb;
+    return SFASTRNN2P_SUCCESS;
+}
+
+unsigned sfastrnn2p_add_new_samples(int16_t *samples, int len){
+    int size = q_getSize(&audioQ);
+    if(AUDIO_SAMPLES_BUFFER_LEN - size < len)
+        return SFASTRNN2P_AUDIO_SAMPLES_BUFFER_FULL;
+    // Try to lock with timeout of 1 ms. Should be enough.
+    int rett = audio_buffer_mutex.lock(1);
+    if(rett != osOK)
+        return SFASTRNN2P_AUDIO_QUEUE_MUTEX_LOCK_ERR;
+    // Don't use force push. We have asserted that there is enough space
+    // The only reason for this to fail is a failed buffer. We want to
+    // be aware of that.
+    int ret = q_enqueue_batch(&audioQ, (void *)samples,
+        sizeof(int16_t), len);
+    audio_buffer_mutex.unlock();
+    if(ret != 0)
+        return SFASTRNN2P_AUDIO_SAMPLES_BUFFER_PUSH_ERR;
+    return SFASTRNN2P_SUCCESS;
+}
+
+void pred_func(){
+    int qsize;
+    int debug_fftTime = 0;
+    while (quit_prediction_flag == 0){
+        qsize = q_getSize(&audioQ);
+        if(qsize < FRAME_LEN){
+            // sleep for 5 ms
+            rtos:wait_ms(5);
+            continue;
+        }
+        while(qsize >= FRAME_LEN){
+            audio_buffer_mutex.lock();
+            qsize = q_getSize(&audioQ);
+            for(int i = 0; i < FRAME_LEN; i++)
+                audio_frame_buffer[i] = (int32_t)(*(int16_t*)q_atN(&audioQ, i));
+            for(int i = 0; i < STRIDE; i++)
+                q_del_oldest(&audioQ);
+            audio_buffer_mutex.unlock();
+            int32_t tailnew = audio_frame_buffer[STRIDE - 1];
+            // Compute FFT and push to feature vector buffer
+            #ifdef FFT_Q31
+            logfbank(mfcc_result, audio_frame_buffer, fbank_q31, preemph_tail);
+            #elif FFT_F32
+            logfbank(mfcc_result, audio_frame_buffer, fbank_f32, preemph_tail);
+            #endif
+            // If required perform normalization
+            #ifdef NORMALIZE_FEAT
+            for(int i = 0; i < NFILT; i++){
+                mfcc_result[i] = (mfcc_result[i] - featNormMean[i]);
+                mfcc_result[i] /= featNormStd[i];
+            }
+            #endif
+            preemph_tail = tailnew;
+            // Feature vector is never full - maintained as an invariant.
+            // If its is full, we are not processing fast enough. Fail
+            int ret = q_enqueue_batch(&featVecQ, mfcc_result,
+                sizeof(float), NFILT);
+            if (ret != 0)
+                error("PredThrErr: Critial: featVecQ push fail (code %d)", ret);
+            // If feature vector buffer is full, flatten and make prediction
+            // and empty. Maintain the invariant that feature_vec will allow
+            // for at least one push
+            if (q_is_full(&featVecQ)){
+                q_flatten_float(&featVecQ, flat_feat_vec);
+                q_reset(&featVecQ);
+                // We have a full feature vector. Make a prediction.
+                SFastRNNInference2(&sfastrnn2_params, flat_feat_vec, final_h);
+                FCInference(fcparams, final_h, logits, 0);
+                prediction_cb(logits, 13);
+            }
+            qsize = q_getSize(&audioQ);
+        }
+    }
+}
+
+void sfastrnn2p_quit(){
+    quit_prediction_flag = 1;
+}
+
+rtos::Thread::State get_thread_state(){
+    return pred_thread.get_state();
+}
\ No newline at end of file
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/lib/sfastrnn_pipeline/sfastrnnpipeline.h b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/sfastrnn_pipeline/sfastrnnpipeline.h
new file mode 100644
index 000000000..5b6fcd692
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/sfastrnn_pipeline/sfastrnnpipeline.h
@@ -0,0 +1,110 @@
+/*
+ * Prediction Pipeline with Shallow FastRNN
+ * ----------------------------------------
+ * 
+ * This is written in CPP for mbed-os
+ * 
+ * Currently supports 2 layer shallow fastRNN fed features
+ * through a 512-point FFT based log-fbank featurizer. 
+ * 
+ */
+#include <arm_math.h>
+#include "../algorithms/sfastrnn.h"
+#include "../algorithms/fc.h"
+#include "../utils/circularq.h"
+#include "../featurizer/logfbank.h"
+#include <Thread.h>
+#include <Mutex.h>
+#include <string.h>
+#include <mbed_wait_api.h>
+#include <mbed_error.h>
+#ifdef DEBUG_MODE
+    #include "../debug_mode/debugmethods.h"
+#endif
+
+#define TIME_STEPS0 8
+#define TIME_STEPS1 6
+#define HID_STATES0 16
+#define HID_STATES1 16
+#define NUM_LABELS  13
+// The length of the container that will be used to
+// hold the feature vector  (number_filt x timesteps0)
+#define FEAT_BUFFER_LEN                           (TIME_STEPS0 * NFILT)
+// The length of the container that will be used to
+// hold the intermediate hidden sates (hiddenDim0 x timesteps1)
+#define H0_BUFFER_LEN                             (TIME_STEPS1 * HID_STATES0)
+// Hidden state 0 len
+#define H0_LEN                                     HID_STATES0
+// The length of the final hidden state. This will be input
+// to the FC layer
+#define FINAL_H_LEN                               HID_STATES1
+// We need a buffer to keep the audio that is pushed
+// in through add_new_samples. This buffer is serviced
+// by the featurizer, which will flush the buffer once
+// featurization is complete.
+#define AUDIO_SAMPLES_BUFFER_LEN                   2048
+// If feature normalization is required
+#ifdef NORMALIZE_FEAT
+    extern float featNormMean[];
+    extern float featNormStd[];
+#endif
+
+
+// Error Codes
+// -----------
+#define SFASTRNN2P_SUCCESS                         0
+// H0_CONTAINER_LEN is defined to an incorrect value
+// This usually means that HID_STATES0, p0->statesLen
+// or p1->timeSteps are wrong.
+#define SFASTRNN2P_H0_BUFFER_LEN_ERR               1
+// The hidden states len of layer 0 and the input len
+// of layer 1 does not match.
+#define SFASTRNN2P_SFASTRNN2_INIT_ERR              2
+#define SFASTRNN2P_AUDIO_SAMPLES_BUFFER_FULL       3
+// This happens when either the buffer is full or when
+// the queue is corrupted. (check source in circularq.c)
+#define SFASTRNN2P_AUDIO_SAMPLES_BUFFER_PUSH_ERR   4
+#define SFASTRNN2P_PRED_THR_SPWAN_ERR              5
+// NFFT Should be >= FRAME_LEN
+#define SFASTRNN2P_NFFT_TOO_SMALL_ERR              6
+// FEAT_BUFFER_LEN should be timeSteps0 * featLen0
+#define SFASTRNN2P_FEAT_BUFFER_LEN_ERR             7
+// FINAL_H_LEN should be statesLen1
+#define SFASTRNN2P_FINAL_H_LEN_ERR                 8
+// H0_BUFFER_LEN should be statesLen0
+#define SFASTRNN2P_H0_LEN_ERR                      9
+// We coun't not get a lock on the audio queue to push
+// new audio even after waiting for 1 ms. These samples
+// will be dropped.
+#define SFASTRNN2P_AUDIO_QUEUE_MUTEX_LOCK_ERR      10
+// Init function called twice. Since we spawn threads
+// internally, we can't support this. A NoOP second init
+// can be supported (that which just returns SUCCESS)
+// but I don't want to enable this behaviour to enforce
+// programmer awareness.
+#define SFASTRNN2P_MULTIPLE_INIT_ERROR             11
+#define SFASTRNN2P_UNINITIALIZED                   12
+// The output len specified by the fc params does
+// not match the ones specified in NUM_LABELS
+#define SFASTRNN2P_FC_OUT_LEN_ERR                  13
+// The output len specified in FINAL_H_LEN does not
+// match the input required by fc parameters.
+#define SFASTRNN2P_FC_IN_LEN_ERR                   14
+
+// Initialize the model with FastRNNParams and FC params
+// This method also starts the prediction thread and waits
+// for audio, pushed through using the add_new_samples
+// method.
+// p0, p1, fc: Model parameters
+// prediction_cb: A call_back function invoked when a *non-0* class
+//      is prediction. The prediction score vector after the
+//      FC layer is passed as arguments along with its length.
+unsigned sfastrnn2p_init(struct FastRNNParams *p0,
+    struct FastRNNParams *p1, struct FCParams *fc,
+    void (*prediction_cb)(float *, int));
+// Add new audio samples to the prediction pipeline.
+unsigned sfastrnn2p_add_new_samples(int16_t *samples, int len);
+// Stop the prediction thread.
+void sfastrnn2p_quit();
+// Return the state of the prediction thread.
+rtos::Thread::State get_thread_state();
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/lib/utils/circularq.c b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/utils/circularq.c
new file mode 100644
index 000000000..f9f82e658
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/utils/circularq.c
@@ -0,0 +1,377 @@
+#include "circularq.h"
+
+int q_init(
+    FIFOCircularQ *q,
+    void *container,
+    int length,
+    void(*cb_write)(void *, int, void*),
+    void* (*cb_read)(void *, int)
+) {
+    q->front = -1;
+    q->back = -1;
+    q->queue = container;
+    q->maxSize = length;
+    q->cb_write = cb_write;
+    q->cb_read = cb_read;
+    return length;
+}
+
+int q_del_oldest(FIFOCircularQ *q) {
+    if (q->front == -1) {
+        return -1; // FALSE
+    }
+    q->back++;
+    q->back %= q->maxSize;
+    if (q->back == q->front) {
+        q->back = -1;
+        q->front = -1;
+    }
+    return 0; // True
+}
+
+int q_enqueue(
+    FIFOCircularQ *q,
+    void *obj) {
+    if (q->front == -1) {
+        // empty
+        q->back = 0;
+        q->front = 0;
+    }
+    else if (q->back == q->front) {
+        // full
+        return -1; // false
+    }
+    q->cb_write(q->queue, q->front, obj);
+    q->front++;
+    q->front %= q->maxSize;
+    q->back %= q->maxSize;
+    return 0;
+}
+
+int q_enqueue_batch(FIFOCircularQ *q, void *obj,
+    size_t obj_size, int len) {
+    if (q_getSize(q) + len > q->maxSize)
+        return -1;
+    else {
+        for (int i = 0; i < len; i++) {
+            int ret = q_enqueue(q, obj + obj_size * i);
+            if (ret != 0)
+                return -2;
+        }
+    }
+    return 0;
+}
+
+void q_force_enqueue(FIFOCircularQ *q, void *obj) {
+    if (q->front == -1) {
+        // empty
+        q->back = 0;
+        q->front = 0;
+    }
+    else if (q->back == q->front) {
+        // full
+        q->back += 1;
+    }
+    q->cb_write(q->queue, q->front, obj);
+    q->front++;
+    q->front %= q->maxSize;
+    q->back %= q->maxSize;
+}
+
+void q_force_enqueue_batch(FIFOCircularQ *q, void *obj,
+    size_t obj_size, int len){
+    for (int i = 0; i < len; i++){
+        q_force_enqueue(q, obj + obj_size * i);
+    }
+}
+
+void* q_atN(FIFOCircularQ *q, int i) {
+    if (q_getSize(q) < i || q_getSize(q) == 0)
+        return NULL;
+
+    int index = (q->back + i) % q->maxSize;
+    return q->cb_read(q->queue, index);
+}
+
+void* q_oldest(FIFOCircularQ *q) {
+    return q_atN(q, 0);
+}
+
+void q_setN(FIFOCircularQ *q, int i, void *obj) {
+    int index = (q->back + i) % q->maxSize;
+    q->cb_write(q->queue, index, obj);
+}
+
+int q_getSize(FIFOCircularQ *q) {
+    if (q->front == -1)
+        return 0;
+    else if (q->front == q->back)
+        return q->maxSize;
+    else if (q->front > q->back)
+        return q->front - q->back;
+    else
+        return q->front + (q->maxSize - q->back);
+}
+
+int q_is_full(FIFOCircularQ *q){
+    return (q_getSize(q) == q->maxSize);
+}
+
+void q_reset(FIFOCircularQ *q){
+    q->front = -1;
+    q->back = -1;
+}
+
+void q_flatten_int(FIFOCircularQ *q, int *dst) {
+    int size = q_getSize(q);
+    for (int i = 0; i < size; i++) {
+        dst[i] = *(int*)q_atN(q, i);
+    }
+}
+
+void q_flatten_float(FIFOCircularQ *q, float *dst) {
+    int size = q_getSize(q);
+    for (int i = 0; i < size; i++) {
+        dst[i] = *(float*)q_atN(q, i);
+    }
+}
+
+// There is no point inlining these functions. Since we are taking
+// the functions address at other places, the compiler might not
+// (and cannot in most cases) inline it.
+inline void cb_write_int(void *container, int index, void *val) {
+    int *_container = (int*)container;
+    _container[index] = *((int*)val);
+}
+
+inline void cb_write_int16(void *container, int index, void *val) {
+    int16_t *_container = (int16_t *)container;
+    _container[index] = *((int16_t*)val);
+}
+
+inline void cb_write_float(void *container, int index, void *val) {
+    float *_container = (float*)container;
+    _container[index] = *((float*)val);
+}
+
+inline void cb_write_char(void *container, int index, void *val) {
+    char *_container = (char*)container;
+    _container[index] = *((char*)val);
+}
+
+inline void* cb_read_int(void *container, int index) {
+    int *_container = (int*)container;
+    return &(_container[index]);
+}
+
+inline void* cb_read_int16(void *container, int index) {
+    int16_t *_container = (int16_t *)container;
+    return &(_container[index]);
+}
+
+void* cb_read_float(void *container, int index) {
+    float *_container = (float*)container;
+    return &(_container[index]);
+}
+
+void* cb_read_char(void *container, int index) {
+    char *_container = (char*)container;
+    return &(_container[index]);
+}
+
+/*
+ * Uncomment the following to perform circularq tests on mxchip.
+ * Do not forget to define the __TEST_CIRCULAR_Q__ preprocessor directive
+ */
+
+#ifdef __TEST_CIRCULAR_Q__
+#include <math.h>
+int test_circularq() {
+    FIFOCircularQ Q;
+    unsigned errorCode = 0;
+    int container[200];
+    if (!q_init(&Q, container, 200, cb_write_int, cb_read_int))
+        errorCode |= 1;
+
+    for (int i = 0; i < 100; i++) {
+        int j = i + 100;
+        q_force_enqueue(&Q, &(j));
+    }
+
+    int size = q_getSize(&Q);
+    if (!(size == 100))
+        errorCode |= 2;
+    for (int i = 0; i < 100; i++) {
+        if (!(*(int*)(q_atN(&Q, i)) == i + 100))
+            errorCode |= 4;
+    }
+
+
+    for (int i = 100; i < 200; i++) {
+        int j = i + 100;
+        q_force_enqueue(&Q, &j);
+    }
+
+    size = q_getSize(&Q);
+    if (!(size == 200))
+        errorCode |= 8;
+    for (int i = 0; i < 200; i++) {
+        if (!(*(int*)q_atN(&Q, i) == i + 100))
+            errorCode |= 16;
+    }
+
+
+    for (int i = 0; i < 100; i++) {
+        int j = i + -10000;
+        q_force_enqueue(&Q, &j);
+    }
+
+    size = q_getSize(&Q);
+    if (!(size == 200))
+        errorCode |= 32;
+    for (int i = 0; i < 100; i++) {
+        if (!(*(int*)q_atN(&Q, i) == i + 100 + 100)) {
+            errorCode |= 64;
+        }
+    }
+    for (int i = 100; i < 200; i++) {
+        if (!(*(int*)q_atN(&Q, i) == i - 100 - 10000)) {
+            errorCode |= 128;
+        }
+    }
+
+    FIFOCircularQ Qf;
+    float eps = 1e-10;
+    float containerf[1000];
+    if (!q_init(&Qf, containerf, 1000, cb_write_float, cb_read_float))
+        errorCode |= 256;
+
+    for (int i = 0; i < 1000; i++) {
+        float j = i + 100.0;
+        q_force_enqueue(&Qf, &(j));
+    }
+
+    size = q_getSize(&Qf);
+    if (!(size == 1000))
+        errorCode |= (1 << 9);
+    for (int i = 0; i < 1000; i++) {
+        float val = *(float*)(q_atN(&Qf, i));
+        if (fabs(val - i - 100.0) >= eps)
+            errorCode |= (1 << 10);
+    }
+
+    for (int i = 100; i < 1000; i++) {
+        float j = (float)i - 1000.0;
+        q_force_enqueue(&Qf, &j);
+    }
+
+    size = q_getSize(&Qf);
+    if (!(size == 1000))
+        errorCode |= (1 << 11);
+
+    for (int i = 0; i < 100; i++) {
+        float val = *(float*)(q_atN(&Qf, i));
+        if (fabs(val - i - 1000) >= eps)
+            errorCode |= (1 << 12);
+    }
+
+    for (int i = 100; i < 1000; i++) {
+        float val = *(float*)(q_atN(&Qf, i));
+        if (fabs(val - i + 1000) >= eps)
+            errorCode |= (1 << 12);
+    }
+
+    // Testing push pop
+    int container3[10];
+    FIFOCircularQ Q3;
+    q_init(&Q3, container3, 10, cb_write_int, cb_read_int);
+
+    for (int i = 0; i < 10; i++) {
+        if (q_enqueue(&Q3, &i)) {
+            errorCode |= (1 << 16);
+        }
+    }
+    for (int i = 10; i < 15; i++) {
+        if (0 == q_enqueue(&Q3, &i)) {
+            errorCode |= (1 << 17);
+        }
+    }
+
+    if(*(int*)q_oldest(&Q3) != 0) errorCode |= (1 << 18);
+    for (int i = 0; i < 10; i++) {
+        int j = *(int*)q_atN(&Q3, i);
+        if (j != i) errorCode |= (1 << 18);
+    }
+
+    q_del_oldest(&Q3); q_del_oldest(&Q3);
+    if((*(int*)q_oldest(&Q3)) != 2) errorCode |= (1 << 19);
+    for (int i = 0; i < 8; i++) {
+        int j = *(int*)q_atN(&Q3, i);
+        if (j - 2 != i) errorCode |= (1 << 19);
+    }
+
+    q_del_oldest(&Q3);
+    for (int i = 0; i < 7; i++){
+        int j = *(int*)q_oldest(&Q3);
+        size = q_getSize(&Q3);
+        if ((size != 7 - i) ||
+            (j != i + 3))
+            errorCode |= (1 << 20);
+        q_del_oldest(&Q3);
+    }
+
+    size = q_getSize(&(Q3));
+    if (size != 0) errorCode |= (1 << 21);
+
+    int *p1, *p2;
+    p1 = (int *)q_atN(&(Q3), 0);
+    p2 = (int *)q_oldest(&Q3);
+    if(p1 != NULL || p2 != NULL) errorCode |= (1 << 22);
+
+    for (int i = 0; i < 19; i++)
+        q_enqueue(&(Q3), &i);
+    for (int i = 0; i < 10; i++) {
+        int k = *(int*)q_oldest(&(Q3));
+        if ((k != i)) errorCode |= (1 << 23);
+        q_del_oldest(&(Q3));
+    }
+    if(q_del_oldest(&(Q3)) != -1) errorCode |= 23;
+
+    // Testing enqueu batch
+    float containerbq[10];
+    FIFOCircularQ bq;
+    q_init(&bq, containerbq, 10, cb_write_float, cb_read_float);
+    float vals[] = {1, 2, 3, 4};
+    q_enqueue_batch(&(bq), vals, sizeof(float), 4);
+    size = q_getSize(&bq);
+    if (size != 4) errorCode |= (1 << 24);
+    if(*(float*)q_oldest(&bq) != 1)
+        errorCode |= (1 << 24);
+
+    vals[0] = 5; vals[1] = 6;
+    q_enqueue_batch(&(bq), vals, sizeof(float), 2);
+    size = q_getSize(&bq);
+    if (size != 6) errorCode |= (1 << 25);
+    vals[0] = 7; vals[1] = 8; vals[2] = 9; vals[3] = 10;
+    q_enqueue_batch(&(bq), vals, sizeof(float), 4);
+    size = q_getSize(&bq);
+    if (size != 10) errorCode |= (1 << 25);
+    for(int i = 0; i < 10; i++){
+        if(fabs(*(float*)q_atN(&bq, i) - i - 1) > eps)
+            errorCode |= 1 << 25;
+    }
+    for(int i = 0; i < 10; i++){
+        float j = *(float*)q_oldest(&bq);
+        if(fabs(j - i - 1) > eps)
+            errorCode |= 1 << 25;
+        q_del_oldest(&bq);
+    }
+    size = q_getSize(&bq);
+    if (size != 0) errorCode |= (1 << 25);
+    q_enqueue_batch(&(bq), vals, sizeof(float), 4);
+    q_enqueue_batch(&(bq), vals, sizeof(float), 4);
+    if(q_enqueue_batch(&(bq), vals, sizeof(float), 4) != -1)
+        errorCode |= (1 << 25);
+    return errorCode;
+}
+#endif //__TEST_CIRCULAR_Q__
\ No newline at end of file
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/lib/utils/circularq.h b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/utils/circularq.h
new file mode 100644
index 000000000..ad14d3f5f
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/utils/circularq.h
@@ -0,0 +1,126 @@
+#pragma once
+/*
+ * WARNING 1: This is not a true circular queue. You can't pop from
+ * the front unless the queue is full. I haven't implemented
+ * a pop front method
+ *
+ * WARNING 2: Not thread-safe
+ */
+
+// @todo: reimplement with just size and not function
+// pointers and memcpy.
+
+#include <stddef.h>
+#include <arm_math.h>
+
+typedef struct StructFIFOCircularQ {
+    void *queue;
+    int front;
+    int back;
+    int maxSize;
+    // (container to edit, index, value)
+    void (*cb_write)(void *, int, void *);
+    // (container to read, index)
+    void* (*cb_read)(void *, int);
+} FIFOCircularQ;
+
+#ifdef __cplusplus
+    extern "C" {
+#endif 
+
+// Since we are being compied as C++
+// Returns the length of the queue (vacuous)
+int q_init(
+    FIFOCircularQ *q, 
+    void* container, 
+    int length,
+    void (*cb_write)(void *, int, void*),
+    void* (*cb_read)(void *, int)
+    );
+//
+// Returns 0 if q is not full, 1 otherwise
+//
+int q_is_full(FIFOCircularQ *q);
+
+//
+// Resets the q
+//
+void q_reset(FIFOCircularQ *q);
+
+//
+// deletes the at(0) element. Does not return it.
+// Use q_oldest + q_del_oldest for that behaviour.
+//
+int q_del_oldest(FIFOCircularQ *q);
+
+// 
+// Pushes element to the back of the buffer
+// Return 0 on success, -1 if buffer full
+//
+int q_enqueue(FIFOCircularQ *q, void *obj);
+
+//
+// Batch version of q_enqueue. Pushes len elements or nothing
+// Specify @obj_size of each object in bytes 
+// For a batch [0, 1, 2, 3], 0 is enqueued first, followed by
+// 1, 2, 3 in that order.
+// Return 0 on success, -1 if buffer full, -2 if q corrupted
+//
+int q_enqueue_batch(FIFOCircularQ *q, void *obj, size_t obj_size, int len);
+
+//
+// q_enqueue and overwrite earliest element if buffer full
+//
+void q_force_enqueue(FIFOCircularQ *q, void *obj);
+//
+// Similar to q_enqueue_batch but forcefully removed earlier elements
+// if queue does not have enough space
+// TODO: Test cases
+void q_force_enqueue_batch(FIFOCircularQ *q, void *obj,
+    size_t obj_size, int len);
+//
+// get element at N-th position in circular buffer
+// 0 - earliest pushed element
+// size - 1: last pushed element
+// Fails if N > size
+//
+void* q_atN(FIFOCircularQ *q, int N);
+
+//
+// get earliest element inserted into the circular buffer
+//
+void* q_oldest(FIFOCircularQ *q);
+
+//
+// set element at N-th position in circular buffer
+// Fails if N > size
+//
+void q_setN(FIFOCircularQ *q, int N, void *obj);
+
+//
+// Returns number of elements in queue
+//
+int q_getSize(FIFOCircularQ *q);
+
+
+void q_flatten_int(FIFOCircularQ *q, int *dst);
+void q_flatten_float(FIFOCircularQ *q, float *dst);
+// data type specific call back
+void cb_write_int(void *container, int index, void *val);
+void cb_write_int16(void *container, int index, void *val);
+void cb_write_float(void *container, int index, void *val);
+void cb_write_char(void *container, int index, void *val);
+void* cb_read_int(void *container, int index);
+void* cb_read_int16(void *container, int index);
+void* cb_read_float(void *container, int index);
+void* cb_read_char(void *container, int index);
+
+#ifdef __TEST_CIRCULAR_Q__
+    You also have to manually uncomment this function
+    declaration in circularq.c
+    int test_circularq();
+#endif
+
+#ifdef __cplusplus
+} // end extern "C"
+#endif
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/lib/utils/helpermath.c b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/utils/helpermath.c
new file mode 100644
index 000000000..05db3b6a1
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/utils/helpermath.c
@@ -0,0 +1,95 @@
+#include "helpermath.h"
+
+/* Left multiply mxn matrix (max) with n-d vector vec */
+void matrixVectorMul(const float *mat, const unsigned m,
+    const unsigned n, const float *vec, float *dst){
+    for (int i = 0; i < m; i++){
+        float dotProd = 0.0f;
+        for(int j = 0; j < n; j++){
+            dotProd += mat[i*n + j] * vec[j];
+        }
+        dst[i] = dotProd;
+    }
+}
+
+void scalarVectorMul(float32_t *dst, const unsigned length,
+    const float32_t scalar) {
+    arm_scale_f32(dst, scalar, dst, length);
+}
+
+void vectorVectorAdd(float *dstVec, const float *srcVec,
+    const unsigned length){
+    for(unsigned i = 0; i < length; i++)
+        dstVec[i] += srcVec[i];
+}
+
+void vectorVectorHadamard(float *dst, const float *src,
+    const unsigned length){
+    for(unsigned i = 0; i < length; i++){
+        dst[i] = dst[i] * src[i];
+    }
+}
+
+float gaussian(const float *x, const float *y,
+    unsigned length, float gamma) {
+    float sumSq = 0.0;
+    for(unsigned i = 0; i < length; i++){
+        sumSq += (x[i] - y[i])*(x[i] - y[i]);
+    }
+    sumSq = -1*gamma*gamma*sumSq;
+    sumSq = exp(sumSq);
+    return sumSq;
+}
+
+void vsigmoid(float *vec, unsigned length){
+    // Refer to:
+    // https://timvieira.github.io/blog/post/2014/02/11/exp-normalize-trick/
+    for (int i=0; i < length; i++){
+        if(vec[i] >= 0){
+            float z = exp(-1 * vec[i]);
+            vec[i] = 1.0 / (1.0 + z);
+        } else {
+            float z = exp(vec[i]);
+            vec[i] = z / (1.0 + z);
+        }   
+    }
+}
+
+
+void vtanh(float *vec, unsigned length){
+    for (int i=0; i < length; i++){
+        vec[i] = tanh(vec[i]);
+    }   
+}
+
+void softmax(float *input, size_t input_len) {
+    //https://codereview.stackexchange.com/questions/180467/implementing-softmax-in-c
+    float m = -INFINITY;
+    for (size_t i = 0; i < input_len; i++) {
+        if (input[i] > m) {
+            m = input[i];
+            }
+    }
+
+    float sum = 0.0;
+    for (size_t i = 0; i < input_len; i++) {
+        sum += expf(input[i] - m);
+    }   
+
+    float offset = m + logf(sum);
+    for (size_t i = 0; i < input_len; i++) {
+        input[i] = expf(input[i] - offset);
+    }
+}
+
+int argmax(float *vec, int len){
+    float max = vec[0];
+    int arg = 0;
+    for(int i = 0; i < len; i++){
+        if (vec[i] > max){
+            max = vec[i];
+            arg = i;
+        }
+    }
+    return arg;
+}
\ No newline at end of file
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/lib/utils/helpermath.h b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/utils/helpermath.h
new file mode 100644
index 000000000..db79d54b4
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/src/lib/utils/helpermath.h
@@ -0,0 +1,30 @@
+#pragma once
+
+#include <stdlib.h>
+#include <arm_math.h>
+
+#ifdef __cplusplus
+	extern "C" {
+#endif 
+
+/* Left multiply mxn matrix (max) with n-d vector vec
+ * input matrix need to be flattened in row major form.
+*/
+void matrixVectorMul(const float *mat, const unsigned m,
+	const unsigned n, const float *vec, float *dst);
+void scalarVectorMul(float32_t *vec, const unsigned length,
+	const float32_t scalar);
+void vectorVectorAdd(float *dstVec, const float *srcVec,
+	const unsigned length);
+void vectorVectorHadamard(float *dst, const float *src,
+	const unsigned length);
+float gaussian(const float *x, const float *y,
+	unsigned length, float gamma);
+void vsigmoid(float *vec, unsigned length);
+void vtanh(float *vec, unsigned length);
+void softmax(float *input, size_t input_len);
+int argmax(float *, int len);
+
+#ifdef __cplusplus
+}
+#endif
\ No newline at end of file
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/models/emi_fastrnn_16_16_88.zip b/Applications/KeywordSpotting/MXChip-SRNN/src/models/emi_fastrnn_16_16_88.zip
new file mode 100644
index 000000000..c230a136c
Binary files /dev/null and b/Applications/KeywordSpotting/MXChip-SRNN/src/models/emi_fastrnn_16_16_88.zip differ
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/models/generateModelParams.py b/Applications/KeywordSpotting/MXChip-SRNN/src/models/generateModelParams.py
new file mode 100644
index 000000000..85e93816c
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/src/models/generateModelParams.py
@@ -0,0 +1,125 @@
+import numpy as np
+from template_MXChip import getTemplate as getTemplateMXChip
+import python_speech_features as sp
+
+
+LABELMAP13 = {
+    'go': 1, 'no': 2, 'on': 3, 'up': 4, 'bed': 5, 'cat': 6,
+    'dog': 7, 'off': 8, 'one': 9, 'six': 10, 'two': 11,
+    'yes': 12,
+    'wow': 0, 'bird': 0, 'down': 0, 'five': 0, 'four': 0,
+    'left': 0, 'nine': 0, 'stop': 0, 'tree': 0, 'zero': 0,
+    'eight': 0, 'happy': 0, 'house': 0, 'right': 0, 'seven': 0,
+    'three': 0, 'marvin': 0, 'sheila': 0, '_background_noise_': 0
+}
+
+np.random.seed(42)
+
+def sigmoid(x):
+    return 1.0 / (1 + np.exp(-x).astype(np.float32))
+
+class FastRNN:
+    def __init__(self, W, U, b, alpha, beta, statesLen):
+        self.statesLen = statesLen
+        self.alpha = alpha
+        self.beta =  beta
+        # Wx + Uh + b
+        self.W, self.U = W, U
+        self.Wfused = np.concatenate([U, W], axis=1)
+        self.b = b
+
+    def cell(self, x, h):
+        Wfused = self.Wfused
+        b = self.b
+        hx = np.concatenate([h, x])
+        h_= np.squeeze(np.matmul(Wfused, hx))
+        h_ = np.squeeze(h_) + b
+        h_ = sigmoid(h_)
+        h_ = self.alpha * h_ + self.beta * h
+        return h_
+
+    def unroll(self, x_list):
+        h = np.zeros(self.statesLen)
+        for x in x_list:
+            h = self.cell(x, h)
+        return h
+
+class Stacked2LayerRNN:
+    def __init__(self, rnn0, rnn1, fcW, fcB):
+        self.rnn0 = rnn0
+        self.rnn1 = rnn1
+        self.fcW = fcW
+        self.fcB = fcB
+    
+    def infer(self, x):
+        '''
+        x: [NUM_BRICKS, NUM_BRICK_TIMESTEPS, NUM_INPUT]
+        '''
+        rnn0, rnn1 = self.rnn0, self.rnn1
+        assert x.ndim == 3
+        h_list = []
+        for brick in x:
+            h = rnn0.unroll(brick)
+            h_list.append(h)
+        h_final = rnn1.unroll(h_list)
+        predictions = np.matmul(self.fcW, h_final) + self.fcB
+        return predictions
+
+
+def main():
+    # Configuration
+    # -------------
+    paramsDir = './params/'
+    timeSteps0 = 8
+    timeSteps1 = 6
+    # -------------
+    # Load the model and initialize two fast RNN cells
+    x_sample = np.load(paramsDir + 'x_sample.npy')
+    target = np.load(paramsDir + 'target_sample.npy')
+    predictions = np.load(paramsDir + 'predicted_sample.npy')
+    mean = np.load(paramsDir + 'mean.npy')
+    std = np.load(paramsDir + 'std.npy')
+
+    fcW = np.load(paramsDir + 'fcW.npy').T
+    fcB = np.load(paramsDir + 'fcB.npy') 
+    W0 = np.load(paramsDir + 'W0.npy').T
+    U0 = np.load(paramsDir + 'U0.npy').T
+    b0 = np.squeeze(np.load(paramsDir + 'h0.npy'))
+    alpha0 = sigmoid(np.squeeze(np.load(paramsDir + 'alpha0.npy')))
+    beta0 = sigmoid(np.squeeze(np.load(paramsDir + 'beta0.npy')))
+    fastRNN0 = FastRNN(W0, U0, b0, alpha0, beta0, W0.shape[0])
+    W1 = np.load(paramsDir + 'W1.npy').T
+    U1 = np.load(paramsDir + 'U1.npy').T
+    b1 = np.squeeze(np.load(paramsDir + 'h1.npy'))
+    alpha1 = sigmoid(np.squeeze(np.load(paramsDir + 'alpha1.npy')))
+    beta1 = sigmoid(np.squeeze(np.load(paramsDir + 'beta1.npy')))
+    fastRNN1 = FastRNN(W1, U1, b1, alpha1, beta1, W1.shape[0])
+
+    srnn2 = Stacked2LayerRNN(fastRNN0, fastRNN1, fcW, fcB)
+    errorCount = 0
+    print("Verifying outputs")
+    for i in range(len(x_sample)):
+        ret = srnn2.infer(x_sample[i])
+        errorCount += int(abs(np.sum(ret - predictions[i])) >= 0.0001)
+    print("Done! Error cound %d for %d tests" % (errorCount, len(x_sample)))
+
+    W0 = np.concatenate([U0, W0], axis=1) 
+    W1 = np.concatenate([U1, W1], axis=1) 
+    assert x_sample.shape[2] == timeSteps0
+    assert x_sample.shape[1] == timeSteps1
+    numOutput = fcW.shape[0]
+    ret = getTemplateMXChip(W0=W0, B0=b0, alpha0=alpha0, beta0=beta0,
+                            W1=W1, B1=b1, alpha1=alpha1, beta1=beta1,
+                            timeSteps0=timeSteps0, timeSteps1=timeSteps1,
+                            fcW=fcW, fcB=fcB, numOutput=numOutput, 
+                            labelMap=LABELMAP13,
+                            normalize=True, mean=mean, std=std)
+    print("Generating model.h")
+    f = open('model.h', 'w+')
+    print(ret, file=f)
+    f.close()
+    print("Done")
+
+
+
+main()
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/models/model.h b/Applications/KeywordSpotting/MXChip-SRNN/src/models/model.h
new file mode 100644
index 000000000..a45fcd8ad
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/src/models/model.h
@@ -0,0 +1,109 @@
+
+#include "../lib/algorithms/sfastrnn.h"
+#include "../lib/algorithms/fc.h"
+#include <arm_math.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+struct FastRNNParams fastrnnParams0;
+struct FastRNNParams fastrnnParams1;
+struct FCParams fcParams;
+
+void initFastRNN0();
+void initFastRNN1();
+void initFC();
+
+#ifdef __cplusplus
+}
+#endif
+static float combinedWMatrix0[] = {
+    -4.394342, 1.670318, -2.444137, 3.987555, -0.367528, 0.847272, -1.579511, -2.468433, -7.282849, -0.976210, 3.635147, 6.876060, -3.495753, -4.241759, 5.861881, -1.029778, -0.132852, 0.482839, -0.773421, -1.058756, -0.400583, 0.313920, 0.754386, 2.407314, 0.084614, -0.621101, -1.202959, -0.817329, 1.091781, 0.803296, -1.132104, -1.491276, -1.285186, -0.367634, -0.191333, 2.019341, 0.813238, 0.842223, 1.320992, 0.335768, -0.458256, 0.623994, -0.636713, 0.135170, 0.722895, -0.540891, 0.339657, -0.178645, 
+-5.010478, -12.386557, 3.541905, 1.687755, -0.226694, 2.467856, -2.713917, -4.092384, 8.001176, 1.380618, -3.027768, 1.509269, 1.249332, 4.262799, 1.940189, 0.479688, -0.647341, -0.274383, 0.621559, 0.840944, 0.471705, 1.386905, 2.151344, 1.836882, 0.543310, 0.328246, -0.259154, -0.031627, 0.408222, -0.686999, -0.324494, 0.529456, -0.041894, 0.162293, 0.634429, 0.658714, 0.309362, 0.206179, 0.323115, -0.444688, 0.045480, 0.334993, -0.038282, -0.554810, 0.144400, -0.221486, 0.129975, -0.449101, 
+-3.118361, -1.812039, -4.206928, 2.821902, -0.748216, 4.338758, -3.047090, -10.049188, 0.120711, -8.117956, 2.690513, -1.699157, 0.425068, 2.030299, -2.204330, -1.336558, -0.831021, -0.696517, 1.643283, 0.811341, 0.816283, 0.673865, -1.026075, -1.222155, -0.589451, -1.283914, -1.704754, -0.735262, 0.259212, 1.528514, 1.482427, 1.216248, 1.072276, 0.873297, 0.285459, -0.135662, 1.117006, -0.472917, 0.991429, -0.119520, 0.087565, -0.214628, -0.061496, -0.169957, 0.019995, -0.054380, -0.090222, -0.428023, 
+-0.242790, 1.813288, 0.119135, -2.969033, -1.675614, -3.901589, -0.091110, -7.544526, 1.960290, -0.037544, -0.672174, 4.773893, 0.150768, -3.041260, 3.332448, -0.244760, 0.657012, -2.135938, 2.968000, -1.933262, -0.587828, -1.612573, -1.273521, -1.634842, 1.549491, 2.493810, 3.421001, 2.132683, -0.197654, -0.376506, -1.291105, 0.537511, -0.426619, -1.155925, -0.591313, -1.176670, 1.275704, 0.069142, -1.310604, 0.469024, 0.390803, 0.219441, -0.309057, 0.795271, -0.377698, -0.204987, 0.098430, -0.072286, 
+1.659412, -0.136527, 0.939389, -3.545981, -3.184775, -2.974060, -0.538432, -3.639727, -0.837568, -1.604512, -0.579743, -4.922958, 1.545775, 0.529332, -7.399548, 4.370611, -0.080237, 1.754189, -0.956228, 0.022387, -1.777416, -0.993705, -2.662503, -1.556300, 1.677594, 1.765419, 2.185814, 2.665101, -1.390469, -0.851602, 0.933666, 1.104553, 2.189019, -0.020495, 1.062610, 0.149326, -1.536042, -2.015504, -0.590997, -0.187154, -0.888261, -0.192437, -0.007773, -0.195747, 0.024274, 0.260669, -0.274831, -0.121948, 
+-0.644862, 3.412475, -5.739103, 0.730205, 1.893141, -5.632226, -0.136634, -3.698142, 1.016315, -0.203452, 1.990294, -4.053802, -2.250560, 2.936410, 0.938369, -4.563478, -0.198095, 1.321754, -1.213177, 0.746386, 0.395729, 0.735425, 0.674619, -0.286715, -0.213787, 0.695955, 1.095577, 2.195754, 0.552678, 0.022346, 0.491991, -1.117851, -2.112240, -1.116424, -0.688602, -0.407817, -0.537197, 0.696354, 0.902197, 0.448935, -0.177437, -0.303541, 0.401238, -0.163165, -0.310976, 0.233415, -0.507773, 0.439906, 
+0.405050, 1.563615, -2.845270, 1.864122, 2.324951, -2.308045, 1.342459, -0.484539, 0.426602, 1.209528, -1.047912, -0.280595, -0.585254, 1.265471, -2.105597, -6.096662, -0.203713, -0.298388, 1.028528, 0.607156, 0.308826, -0.618201, -0.722835, -1.619279, -0.824562, -0.617465, -0.543220, -1.233350, -2.441530, -1.802619, 2.627425, 2.585896, 1.069127, 1.195297, -1.627771, 0.595738, 1.037760, 0.417393, -0.090837, -0.146070, -0.176088, 1.056672, -0.459774, 0.658548, -0.328070, -0.268748, -0.390144, 0.451860, 
+-1.733371, -2.615444, -3.020136, -1.453281, 2.591630, -2.385435, -0.073184, -3.689105, -2.830683, -0.677763, 2.251215, 1.987840, -0.602462, -6.297771, 1.119586, -0.775122, -0.517076, -1.090677, 2.195573, 3.490596, 3.532367, 1.302304, -0.036746, -2.973045, -0.614896, -1.700183, -0.836986, -0.260505, -0.024295, 0.439021, -0.761616, -1.531463, -1.656251, 0.141997, -0.249461, 1.028342, 1.568828, 0.854612, 0.420676, 0.125925, -0.075899, 0.404789, 0.054414, -0.723230, 0.321348, -0.352063, -0.073613, 0.100152, 
+-0.264005, -1.951415, -1.429368, -1.594596, -0.371441, 0.040082, 1.656364, 2.827784, -8.510094, -3.045576, 0.982709, -0.357535, 0.458934, -5.671675, 2.810453, -2.509865, -0.055260, 0.149244, -2.556470, -0.289469, -0.956199, -0.967695, -1.253180, 0.375230, 0.470758, -0.480561, -1.188382, -0.385000, 0.729667, 1.763392, 0.788664, -0.381417, -0.188639, 0.700852, -0.828685, -0.422568, -0.385014, -0.557915, -0.905237, -0.235940, 0.497495, 0.208721, 0.115759, 0.290364, 0.086713, -0.006929, -0.818390, 1.248167, 
+-2.651732, -0.196781, 2.481395, -1.427539, -3.650820, -4.447654, 3.010999, -2.660051, 0.965630, -7.510485, -3.075092, -0.512343, 0.263273, 3.732776, -0.218930, -3.831048, 0.297317, -1.412834, -1.033311, 0.403555, 1.724447, 2.214988, 3.159698, 0.195241, -2.543757, -1.156173, -0.658126, -0.196266, -0.891090, -0.997414, -0.669083, -1.452945, 0.267728, 0.721529, 1.612172, 0.383177, 1.040189, 0.653729, 0.163864, -0.094382, 0.666856, 0.253287, 0.024207, -0.089818, -0.387213, -0.115701, 0.229131, -0.504887, 
+3.510151, 13.682715, -1.262339, -0.938372, 0.214310, -4.568920, 1.900012, 0.168166, -1.113414, 4.860772, -3.513642, -7.361302, 0.977453, 3.073361, 0.495730, 1.790086, 0.665338, 0.358030, 1.767592, 1.373327, -0.377032, -1.457430, -0.920559, 0.233391, 1.578097, 1.341954, 0.032383, 1.021583, 0.113483, -0.202995, 0.503583, 0.312998, -0.546984, 0.279893, 0.275056, -1.264586, 1.479502, -0.670943, 0.622271, -0.101685, 0.061433, -2.146494, -0.167393, -1.107415, -1.833909, -2.074111, -1.063813, -2.169270, 
+-2.608964, -4.021140, -0.128930, -4.680740, -3.001136, 1.165962, -2.217025, 1.472322, 1.831619, -2.566680, 2.990274, -9.275423, -1.245824, 3.842207, 0.191467, -2.173995, -0.083123, -0.301617, -1.096413, -0.374791, -1.169248, 0.193628, 0.578226, 0.233941, 0.187847, 0.837287, -0.367849, 0.188699, 1.307282, 2.631428, 3.713660, 2.160410, 1.891309, -0.735538, -1.449885, -1.196488, -0.918136, -0.692220, -1.113876, -0.364902, 0.377525, 0.318798, -0.076559, -0.037340, -0.291767, -0.562892, 0.066101, -0.191011, 
+1.705875, -2.202471, -0.187324, -0.836479, 0.115738, 4.280789, 0.098201, -0.026486, 3.779680, 0.790323, -0.340409, -1.200357, 0.258244, 3.573425, -2.039930, 0.448585, -0.263841, 1.054291, -0.734569, 0.357012, 0.344547, -0.227532, -1.293920, -2.773390, -0.967389, -0.291874, 1.814994, 1.863690, 2.086262, 2.177715, -0.059085, -0.779559, 0.034168, -1.790127, 0.128300, -0.917041, -0.207902, -0.319050, 0.061688, 1.112218, -0.369199, -0.069757, -0.304790, 0.515871, 0.195216, -0.047352, 0.470148, 0.135613, 
+-1.801733, -14.333292, -6.710084, 2.027344, -0.738840, 2.533691, -1.460382, 0.617199, 3.300643, -1.117942, 3.094809, 1.257551, -3.057455, -7.443308, 0.006572, -0.762965, -0.756610, 1.186737, -0.927841, -0.341335, -0.853035, 0.217751, 0.376304, 2.052606, 1.383751, 0.387989, 0.656082, 0.743186, -0.085105, -0.018542, 0.111765, -0.176987, 0.164592, -0.040699, -0.555956, 1.440574, -0.231119, 1.188938, 1.542780, -0.064015, -0.069175, 0.515627, 0.091999, -0.237204, 0.515334, -0.054009, 0.603516, -0.318698, 
+-2.384683, 2.217553, -0.510579, -3.762332, 0.927062, 1.052021, -0.594735, 2.678587, 5.472569, -5.223604, 0.411627, 0.326158, 3.073533, 0.891218, -7.836277, -0.877050, 0.337595, 0.027045, 0.207879, 1.107072, 2.751967, 3.531062, 1.655158, -3.068271, -3.484626, -1.509957, -0.649874, -0.190971, -1.526214, -1.772561, 0.418379, 0.525252, 0.049277, 1.424461, 0.954263, -0.375417, -1.519454, 0.281435, 0.668371, 0.240059, 0.549209, 0.566503, -0.102672, -0.965943, 0.562778, -0.635043, 1.523030, -0.806144, 
+-4.007565, -2.384225, 1.985205, 5.132339, -1.038108, -3.017178, 6.698817, 0.000970, -4.567681, -0.637183, -1.207071, 1.972803, -5.824776, 2.519549, 5.051178, -10.033369, -0.685569, -0.454371, -0.362847, -0.009798, 0.080245, -0.475046, -0.687661, 0.673480, 1.079727, 0.344966, -1.493761, -1.561329, 0.848099, 3.756004, 4.460750, 0.785049, -1.214222, -1.517126, -1.006831, -0.269839, 0.436658, 0.493897, -0.802526, -0.039728, 0.621665, -0.144493, -0.240633, 0.607524, -0.736513, 0.338355, -0.749308, 0.594335, 
+
+ };
+
+static float combinedBMatrix0[] = {-0.483399,-5.375202,-0.894719,-2.971153,0.176196,-4.627392,2.712877,-3.889010,3.451340,-0.512680,3.635937,-2.563429,-1.778029,-1.228812,-4.482069,-2.371642,};
+
+static float combinedWMatrix1[] = {
+    -8.993324, 5.766108, -0.984275, 1.232846, -0.784503, -1.215732, -1.109800, -1.419134, 0.867878, 0.985043, 1.606076, -0.275711, 1.211763, -0.110211, 2.418703, -2.073111, 1.863143, -4.919676, -2.105419, 3.112989, 3.771616, 3.573565, -0.141503, -1.749448, 2.866004, 1.695223, -2.021968, -1.316671, 1.155566, 2.337256, -0.808888, 0.022269, 
+1.040456, -5.227429, -0.590861, 2.846540, 2.510352, 8.778857, 0.772251, 0.013926, 1.175662, -0.919632, 1.295280, 3.194552, -0.671261, -2.404313, 2.420117, 0.925858, -1.226532, -0.982795, -2.969703, 0.164707, -0.217380, -0.768241, -0.376959, 2.921228, 3.840075, 2.656377, 0.184129, 4.891365, -0.248023, -1.842269, 2.994883, -0.866867, 
+-2.433946, 2.733418, -7.199495, 1.227334, -4.087968, -4.372028, 7.848134, -2.760289, 3.304864, -1.298907, 1.932202, -0.909907, 0.924740, -1.356102, -5.423501, -3.767486, 0.325634, -2.054650, 2.083872, 0.728384, 1.154159, 0.952869, 1.352677, 4.639591, -0.716756, 0.798336, -1.293890, 1.404328, -0.389740, -0.894149, 0.415447, -0.260267, 
+-0.407898, 0.768603, 1.095211, -4.719667, -3.602338, 3.734942, 1.431481, 2.184455, 0.390121, 1.675800, -0.604315, 0.434035, 1.974323, -1.010169, 0.466087, 0.810820, -3.064052, 1.466479, 0.240817, -1.555377, 1.680161, 1.640082, -2.491495, -3.465886, 0.882935, -0.745272, 3.704679, 4.501615, 0.154132, -2.079676, -0.336283, 1.146916, 
+1.872181, -0.171051, 0.428701, 0.277004, -6.850987, 0.018607, -1.209911, 5.147545, 2.395395, -0.063215, -3.504235, -0.782541, -1.775267, -1.429212, -1.090621, 3.328624, -0.369251, 0.046201, 0.682975, -1.618495, 1.281623, -2.842376, 0.478221, 2.351111, -2.736117, -1.092621, 1.578171, 4.780344, 0.432083, -0.790555, -2.146031, 0.992934, 
+2.229246, -3.632861, 2.121870, 1.937214, 0.773552, -7.277787, 1.350840, 3.131287, 0.261730, -2.937069, 1.273218, 2.050651, -2.149755, -3.658037, 1.927494, 4.298773, -0.149977, 3.697038, -0.205383, -2.551378, -0.561584, -2.299595, 1.620943, 0.698497, -0.871604, -1.842371, -2.693478, -1.622483, 2.097851, -5.380957, -2.524892, 0.540286, 
+1.974882, 0.860660, 0.483594, -4.428236, 0.254198, -0.738776, -7.166861, 4.724154, -0.094901, 3.128331, -0.223106, 0.047523, -1.512438, -0.349958, 1.349454, -1.073704, 0.963849, -7.747976, 2.641978, 1.012649, 0.867783, 3.279111, -1.728747, -1.165767, -1.853621, 2.364444, -0.712560, 1.917849, 0.494486, 1.950737, -0.027571, 0.073560, 
+0.185064, 2.787849, 1.392283, -3.992270, -2.176939, 1.571382, -3.505462, -10.164021, -1.369556, 1.395783, -2.141547, -5.636579, 3.930323, 0.386195, -6.129729, -1.326905, -0.328154, 1.842927, -4.650984, -1.256178, 1.218856, -2.946430, -0.695355, -0.041204, -2.726118, 2.575443, 1.594451, -1.125996, -0.521107, 1.731262, 1.239454, 1.648101, 
+-1.645357, 2.483098, 1.378480, -1.034285, 0.286449, 1.968899, -0.603014, 4.894711, -4.844470, 4.013346, -5.933038, 3.384772, 5.367784, 0.994224, -2.253595, -0.450002, 0.113909, -9.981983, 2.174092, 0.458702, -0.550130, 4.121709, 0.055792, 3.381026, 0.224217, -2.882266, -0.534291, 0.376481, 0.097206, -1.507773, 0.389125, -0.527342, 
+1.772734, -2.101506, 2.286718, 3.837304, -0.114564, -0.330263, 1.073511, 0.029877, -1.633782, -7.562314, -0.648720, 1.629470, 3.975489, 2.423227, 6.425655, 7.710009, 0.337786, -3.375684, -1.860515, 0.107860, 1.165393, -0.612101, 0.055045, -1.761673, 1.279403, 5.502572, -0.987779, 1.097176, 0.532789, -5.638363, 2.125039, 0.035663, 
+-1.731669, 1.573543, -1.810375, 2.018543, -4.017689, -2.031807, 2.143212, 0.437938, 2.074214, -2.458002, -7.663488, 2.675484, 7.721604, -3.649217, -3.664882, 4.330673, 0.434385, 3.215706, 1.291247, -1.357334, 1.918163, -1.578749, 0.887113, 0.056447, 4.543562, -2.219110, -0.812309, -1.380378, 0.706664, -1.992625, -0.605155, 1.648007, 
+-0.745284, -0.395060, 2.257222, -2.050334, -2.811226, -3.223480, -0.935113, 4.670177, -0.166676, 0.190502, 4.950702, -5.055352, -2.926551, -0.875040, -2.181215, 2.516552, 3.281529, 4.631181, 0.170389, -1.290764, -1.637012, 1.986015, 2.599300, -0.560773, -1.093340, -2.086372, -2.141530, 5.056557, -1.756402, -4.758149, -0.287663, -1.831596, 
+3.072729, -2.615700, 4.131379, -2.967680, 0.447018, 4.740140, -4.009865, 0.703566, -0.613143, 2.639772, -1.038132, 0.775154, -1.156957, 0.229450, 2.784570, -0.660779, 2.145897, 0.258853, -0.768564, -1.063183, 0.617708, -0.135477, -3.456913, 0.023365, 1.792191, 0.239879, 3.347177, -7.541625, 0.274615, -0.131922, -0.664101, 2.145448, 
+-1.414440, 1.015114, 1.871301, -1.742095, 1.538365, 1.129859, -0.626152, -0.138937, 1.232638, -1.846977, 5.286344, -2.819705, -3.333694, -6.735012, -0.789252, -1.318035, 0.881870, 1.963813, -2.723712, -1.828124, -0.737036, -2.770875, -1.962267, -6.279300, -2.201661, -5.280858, 0.328632, -4.216747, 2.882310, 0.202990, -1.582065, 6.267218, 
+-1.148481, -1.254931, 1.314193, 0.969432, 0.429214, -3.153016, -1.708260, 4.345199, -0.671442, 1.874414, 0.090322, -0.330771, -0.801841, 0.734000, -7.610411, 1.416348, 0.229478, 0.114050, -2.328229, 0.822486, -0.594291, 1.357512, -4.062139, -0.165713, -2.305657, 2.479547, 1.950884, -4.972633, 1.115902, -4.844807, 0.812774, 6.339092, 
+1.136031, 1.501339, -2.519958, 1.596872, 0.409924, -5.161640, -2.744563, -2.843511, -1.029676, -3.751853, 5.121879, -2.964035, -1.117965, 0.380782, -2.735341, -6.494120, -1.351529, -1.072190, 4.475897, -0.367306, -0.331998, 4.780028, 0.935141, 0.891659, -0.969988, 2.362436, -2.633953, 0.126173, 0.406457, 0.058628, -4.265975, -1.729679, 
+
+ };
+static float combinedBMatrix1[] = {0.390521,-1.568006,-1.247215,2.683117,-5.352313,1.015610,-2.181136,-2.871274,4.206901,2.940511,-0.876600,1.264730,2.177765,-0.583102,-1.096310,0.976021,};
+
+static float fcW[] = {1.153998, 2.908503, 2.931298, -6.591868, 8.570377, 1.777791, 4.249845, -5.324548, -1.933409, 1.652255, -3.008340, 2.561109, 5.091272, 2.375975, 0.114272, 1.825302, 
+4.079215, 1.132804, 1.410711, 3.920387, -2.375091, -3.254151, -0.664488, 1.958657, -3.064375, 1.514691, -4.174952, 4.311438, -3.373156, 5.219816, 7.911401, -6.436582, 
+2.892433, 0.543585, 0.357800, -1.411686, 5.022547, -2.542740, -4.708998, 3.959384, 5.811014, -6.125777, -3.734309, -1.885103, 1.591531, 8.301410, 4.805660, -5.946973, 
+-8.308530, 2.225836, 2.609653, 1.061591, -1.923773, -0.394133, 2.562978, -13.416260, 0.970302, -4.682763, 2.798755, -4.203907, 3.902050, 0.464174, -0.507031, 9.735559, 
+2.582557, 4.024098, -3.741822, 1.387635, -0.826144, -4.692614, -2.862955, -8.454850, -4.566405, -9.172646, 7.517973, -2.362217, 1.423986, 12.640054, 0.416312, -1.751263, 
+-2.243015, -5.913934, -0.829215, 4.400629, -6.072413, -0.354700, -3.499763, 3.878453, -0.876954, -1.165360, 0.531110, 5.968567, 4.987993, 1.700330, -9.198635, -6.721683, 
+4.435347, -6.062106, -4.835220, -3.835865, 12.558668, -4.110910, -5.846931, 4.634123, -0.288808, 10.246997, 3.454742, -2.433312, -2.960399, -10.358557, -8.287789, 3.800376, 
+-3.147445, -1.744314, 1.603617, 7.252875, -7.243377, -3.880891, 12.423599, 5.735993, -3.459105, 2.935940, 4.733056, -4.282575, -7.891159, 4.837924, 3.405554, -2.671608, 
+3.892418, -1.989835, 0.918071, 0.043651, -7.037201, 10.196301, 3.654709, -10.874338, -1.109066, -7.223512, 4.990910, -5.295347, 2.799649, -0.427754, 2.969462, 1.733097, 
+-8.279483, 1.865549, 3.969562, 4.548631, -0.153620, 4.558271, 10.215759, -1.037572, 4.205336, -7.001508, -3.296622, -1.629840, 5.132443, -9.705027, -2.475006, 5.934279, 
+-1.256917, 2.953662, -3.081605, 1.836611, -2.805650, 4.750621, -0.547419, 5.925297, 3.857441, 7.218900, -3.965770, 7.754599, -11.122416, -3.871731, -6.957188, -3.133772, 
+-4.082258, 6.178590, -0.460052, -0.987127, 2.318068, -8.007840, -7.212764, 4.920698, -1.504554, 9.524718, -4.809536, -1.719856, -2.680660, -8.458871, 9.215178, 7.220995, 
+9.246867, -5.056426, -0.473073, -11.486769, 4.229325, 8.818913, -1.797206, 8.909225, 7.959111, 1.329669, -2.135564, 0.937130, 1.458388, -4.138429, -2.824711, -4.755872, 
+};
+static float fcB[] = {3.389326,-3.066761,0.569489,2.105324,-0.605301,2.095950,-3.786515,0.042079,-2.827516,-0.433185,2.729823,-2.976138,-2.544198,};
+
+void initFastRNN0() {
+    fastrnnParams0.timeSteps = 8;
+    fastrnnParams0.featLen = 32;
+    fastrnnParams0.statesLen = 16;
+    fastrnnParams0.W = combinedWMatrix0;
+    fastrnnParams0.b = combinedBMatrix0;
+    fastrnnParams0.alpha = 0.371637;
+    fastrnnParams0.beta = 0.892619;}
+
+void initFastRNN1() {
+    fastrnnParams1.timeSteps = 6;
+    fastrnnParams1.featLen = 16;
+    fastrnnParams1.statesLen = 16;
+    fastrnnParams1.W = combinedWMatrix1;
+    fastrnnParams1.b = combinedBMatrix1;
+    fastrnnParams1.alpha = 0.393828;
+    fastrnnParams1.beta = 0.968985;
+}
+
+void initFC(){
+    fcParams.W = fcW;
+    fcParams.B = fcB;
+    fcParams.inputDim = 16;
+    fcParams.outputDim = 13;
+}
+
+
+float featNormMean[] = {3.558071,5.307965,6.723956,6.980298,7.084899,7.346985,7.488606,7.989687,7.914260,7.832272,7.907389,7.931001,7.815156,7.930761,8.122243,8.164267,8.300410,8.296384,8.287304,8.508408,8.552395,8.582291,8.675160,8.630129,8.566705,8.476798,8.394355,8.335714,8.255317,8.179764,7.816524,6.466621,};
+float featNormStd[] = {5.316042, 5.429762, 5.761431, 5.828824, 5.859360, 6.028826, 6.098757, 6.153158, 6.109560, 5.981027, 5.866415, 5.777138, 5.708584, 5.648618, 5.630316, 5.599686, 5.539146, 5.473228, 5.465712, 5.505444, 5.488520, 5.451288, 5.437470, 5.387079, 5.305981, 5.239823, 5.195953, 5.158002, 5.121702, 5.103227, 5.064711, 4.885242, };
+const char *labelInvArr[] = {"Noise", "go","no","on","up","bed","cat","dog","off","one","six","two","yes",};
+
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/src/models/template_MXChip.py b/Applications/KeywordSpotting/MXChip-SRNN/src/models/template_MXChip.py
new file mode 100644
index 000000000..f9e61dfd0
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/src/models/template_MXChip.py
@@ -0,0 +1,140 @@
+def getTemplate(W0, B0, alpha0, beta0, W1, B1, alpha1, beta1, timeSteps0,
+                timeSteps1, fcW, fcB, numOutput, labelMap, normalize=False,
+                mean=None, std=None):
+    statesLen0 = len(B0)
+    featLen0 = W0.shape[1] - statesLen0
+    statesLen1 = len(B1)
+    featLen1 = W1.shape[1] - statesLen1
+    assert W0.shape[0] == statesLen0
+    assert W1.shape[0] == statesLen1
+    assert featLen1 == statesLen0
+    assert fcW.shape[0] == numOutput
+    assert fcW.shape[1] == statesLen1
+    assert fcW.ndim == 2
+    assert fcB.ndim == 1
+    assert fcB.shape[0] == numOutput
+
+    W0Str = ''
+    for i in range(W0.shape[0]):
+        for j in range(W0.shape[1]):
+            W0Str += '%f, ' % W0[i][j]
+        W0Str += '\n'
+
+    B0Str = ''
+    for i in range(B0.shape[0]):
+        B0Str += '%f,' % B0[i]
+
+    W1Str = ''
+    for i in range(W1.shape[0]):
+        for j in range(W1.shape[1]):
+            W1Str += '%f, ' % W1[i][j]
+        W1Str += '\n'
+
+    B1Str = ''
+    for i in range(B1.shape[0]):
+        B1Str += '%f,' % B1[i]
+
+    FCWStr = ''
+    for i in range(fcW.shape[0]):
+        for j in range(fcW.shape[1]):
+            FCWStr += '%f, ' % fcW[i][j]
+        FCWStr += '\n'
+
+    FCBStr = ''
+    for i in range(fcB.shape[0]):
+        FCBStr += '%f,' % fcB[i]
+
+    retStr = '''
+#include <sfastrnn.h>
+#include <fc.h>
+#include <arm_math.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+struct FastRNNParams fastrnnParams0;
+struct FastRNNParams fastrnnParams1;
+struct FCParams fcParams;
+
+void initFastRNN0();
+void initFastRNN1();
+void initFC();
+
+#ifdef __cplusplus
+}
+#endif
+static float combinedWMatrix0[] = {
+    %s
+ };
+
+static float combinedBMatrix0[] = {%s};
+
+static float combinedWMatrix1[] = {
+    %s
+ };
+static float combinedBMatrix1[] = {%s};
+
+static float fcW[] = {%s};
+static float fcB[] = {%s};
+
+void initFastRNN0() {
+    fastrnnParams0.timeSteps = %d;
+    fastrnnParams0.featLen = %d;
+    fastrnnParams0.statesLen = %d;
+    fastrnnParams0.W = combinedWMatrix0;
+    fastrnnParams0.b = combinedBMatrix0;
+    fastrnnParams0.alpha = %f;
+    fastrnnParams0.beta = %f;}
+
+void initFastRNN1() {
+    fastrnnParams1.timeSteps = %d;
+    fastrnnParams1.featLen = %d;
+    fastrnnParams1.statesLen = %d;
+    fastrnnParams1.W = combinedWMatrix1;
+    fastrnnParams1.b = combinedBMatrix1;
+    fastrnnParams1.alpha = %f;
+    fastrnnParams1.beta = %f;
+}
+
+void initFC(){
+    fcParams.W = fcW;
+    fcParams.B = fcB;
+    fcParams.inputDim = %d;
+    fcParams.outputDim = %d;
+}
+
+''' % (W0Str, B0Str, W1Str, B1Str, FCWStr, FCBStr, timeSteps0, featLen0,
+       statesLen0, alpha0, beta0, timeSteps1, featLen1, statesLen1, alpha1,
+       beta1, statesLen1, numOutput)
+
+    if normalize == True:
+        meanStr = ''
+        for val in mean:
+            meanStr += '%f,' % val
+        stdStr = ''
+        for val in std:
+            stdStr += '%f, ' % val
+        str2 = '''
+float featNormMean[] = {%s};
+float featNormStd[] = {%s};
+''' % (meanStr, stdStr)
+        retStr += str2
+    label_inv = {}
+    for key in labelMap:
+        val = labelMap[key]
+        if val in label_inv:
+            label_inv[val].append(key)
+        else:
+            label_inv[val] = [key]
+    assert len(label_inv) == numOutput
+    for i in range(1, numOutput):
+        assert len(label_inv[i]) == 1
+    
+    label_inv_str = 'const char *labelInvArr[] = {"Noise", '
+    for i in range(1, numOutput):
+        label_inv_str += '"%s",' % label_inv[i][0];
+    label_inv_str += "};\n"
+    retStr += label_inv_str;
+    return retStr
+
+
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/src/generateModelParams.py b/Applications/KeywordSpotting/MXChip-SRNN/test/src/generateModelParams.py
new file mode 100644
index 000000000..85e93816c
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/src/generateModelParams.py
@@ -0,0 +1,125 @@
+import numpy as np
+from template_MXChip import getTemplate as getTemplateMXChip
+import python_speech_features as sp
+
+
+LABELMAP13 = {
+    'go': 1, 'no': 2, 'on': 3, 'up': 4, 'bed': 5, 'cat': 6,
+    'dog': 7, 'off': 8, 'one': 9, 'six': 10, 'two': 11,
+    'yes': 12,
+    'wow': 0, 'bird': 0, 'down': 0, 'five': 0, 'four': 0,
+    'left': 0, 'nine': 0, 'stop': 0, 'tree': 0, 'zero': 0,
+    'eight': 0, 'happy': 0, 'house': 0, 'right': 0, 'seven': 0,
+    'three': 0, 'marvin': 0, 'sheila': 0, '_background_noise_': 0
+}
+
+np.random.seed(42)
+
+def sigmoid(x):
+    return 1.0 / (1 + np.exp(-x).astype(np.float32))
+
+class FastRNN:
+    def __init__(self, W, U, b, alpha, beta, statesLen):
+        self.statesLen = statesLen
+        self.alpha = alpha
+        self.beta =  beta
+        # Wx + Uh + b
+        self.W, self.U = W, U
+        self.Wfused = np.concatenate([U, W], axis=1)
+        self.b = b
+
+    def cell(self, x, h):
+        Wfused = self.Wfused
+        b = self.b
+        hx = np.concatenate([h, x])
+        h_= np.squeeze(np.matmul(Wfused, hx))
+        h_ = np.squeeze(h_) + b
+        h_ = sigmoid(h_)
+        h_ = self.alpha * h_ + self.beta * h
+        return h_
+
+    def unroll(self, x_list):
+        h = np.zeros(self.statesLen)
+        for x in x_list:
+            h = self.cell(x, h)
+        return h
+
+class Stacked2LayerRNN:
+    def __init__(self, rnn0, rnn1, fcW, fcB):
+        self.rnn0 = rnn0
+        self.rnn1 = rnn1
+        self.fcW = fcW
+        self.fcB = fcB
+    
+    def infer(self, x):
+        '''
+        x: [NUM_BRICKS, NUM_BRICK_TIMESTEPS, NUM_INPUT]
+        '''
+        rnn0, rnn1 = self.rnn0, self.rnn1
+        assert x.ndim == 3
+        h_list = []
+        for brick in x:
+            h = rnn0.unroll(brick)
+            h_list.append(h)
+        h_final = rnn1.unroll(h_list)
+        predictions = np.matmul(self.fcW, h_final) + self.fcB
+        return predictions
+
+
+def main():
+    # Configuration
+    # -------------
+    paramsDir = './params/'
+    timeSteps0 = 8
+    timeSteps1 = 6
+    # -------------
+    # Load the model and initialize two fast RNN cells
+    x_sample = np.load(paramsDir + 'x_sample.npy')
+    target = np.load(paramsDir + 'target_sample.npy')
+    predictions = np.load(paramsDir + 'predicted_sample.npy')
+    mean = np.load(paramsDir + 'mean.npy')
+    std = np.load(paramsDir + 'std.npy')
+
+    fcW = np.load(paramsDir + 'fcW.npy').T
+    fcB = np.load(paramsDir + 'fcB.npy') 
+    W0 = np.load(paramsDir + 'W0.npy').T
+    U0 = np.load(paramsDir + 'U0.npy').T
+    b0 = np.squeeze(np.load(paramsDir + 'h0.npy'))
+    alpha0 = sigmoid(np.squeeze(np.load(paramsDir + 'alpha0.npy')))
+    beta0 = sigmoid(np.squeeze(np.load(paramsDir + 'beta0.npy')))
+    fastRNN0 = FastRNN(W0, U0, b0, alpha0, beta0, W0.shape[0])
+    W1 = np.load(paramsDir + 'W1.npy').T
+    U1 = np.load(paramsDir + 'U1.npy').T
+    b1 = np.squeeze(np.load(paramsDir + 'h1.npy'))
+    alpha1 = sigmoid(np.squeeze(np.load(paramsDir + 'alpha1.npy')))
+    beta1 = sigmoid(np.squeeze(np.load(paramsDir + 'beta1.npy')))
+    fastRNN1 = FastRNN(W1, U1, b1, alpha1, beta1, W1.shape[0])
+
+    srnn2 = Stacked2LayerRNN(fastRNN0, fastRNN1, fcW, fcB)
+    errorCount = 0
+    print("Verifying outputs")
+    for i in range(len(x_sample)):
+        ret = srnn2.infer(x_sample[i])
+        errorCount += int(abs(np.sum(ret - predictions[i])) >= 0.0001)
+    print("Done! Error cound %d for %d tests" % (errorCount, len(x_sample)))
+
+    W0 = np.concatenate([U0, W0], axis=1) 
+    W1 = np.concatenate([U1, W1], axis=1) 
+    assert x_sample.shape[2] == timeSteps0
+    assert x_sample.shape[1] == timeSteps1
+    numOutput = fcW.shape[0]
+    ret = getTemplateMXChip(W0=W0, B0=b0, alpha0=alpha0, beta0=beta0,
+                            W1=W1, B1=b1, alpha1=alpha1, beta1=beta1,
+                            timeSteps0=timeSteps0, timeSteps1=timeSteps1,
+                            fcW=fcW, fcB=fcB, numOutput=numOutput, 
+                            labelMap=LABELMAP13,
+                            normalize=True, mean=mean, std=std)
+    print("Generating model.h")
+    f = open('model.h', 'w+')
+    print(ret, file=f)
+    f.close()
+    print("Done")
+
+
+
+main()
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/src/main.cpp b/Applications/KeywordSpotting/MXChip-SRNN/test/src/main.cpp
new file mode 100644
index 000000000..388f86812
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/src/main.cpp
@@ -0,0 +1,124 @@
+#include <sfastrnnpipeline.h>
+#include <model.h>
+#include <Arduino.h>
+#include <AudioClassV2.h>
+
+extern struct FastRNNParams fastrnnParams0;
+extern struct FastRNNParams fastrnnParams1;
+extern struct FCParams fcParams;
+extern void initFastRNN0();
+extern void initFastRNN1();
+extern void initFC();
+extern const char *labelInvArr[];
+// A circular q for voting
+#define VOTE_WIN_LEN 10
+#define VOTE_MAJORITY 5
+FIFOCircularQ votingQ;
+static int votingContainer[VOTE_WIN_LEN];
+static int votingFrequence[NUM_LABELS];
+
+// TODO: Explain this
+#define TRANSFER_BUFFER_MAX_LEN 128
+static AudioClass& Audio = AudioClass::getInstance();
+char readBuffer[AUDIO_CHUNK_SIZE];
+static int transfer_buffer_curr_len = 0;
+static int16_t transfer_buffer[TRANSFER_BUFFER_MAX_LEN];
+
+void recordCallback(void) {
+    int length = Audio.readFromRecordBuffer(readBuffer, AUDIO_CHUNK_SIZE);
+    // We are 16bit (short) and not 8bit (char). Hence actual number of samples
+    // is half. Further, we need to ignore the second channel in the
+    // audio (interleaved with the first channel).
+    length = length / 2;
+    length = length - (length % 2);
+    length = length / 2;
+    if(length > TRANSFER_BUFFER_MAX_LEN)
+        error("Transfer buffer too small");
+    // Convert to 16 bit samples
+    int16_t *tempAudio = (int16_t*)readBuffer;
+    if (transfer_buffer_curr_len != 0) {
+        Serial.printf("Error: Transfer buffer not empty. %d dropped\n", length);
+        return;
+    }
+    // Drop every other sample (the second channel) while copying
+    for(int i = 0; i < length; i++)
+        transfer_buffer[i] = tempAudio[2 * i];
+    transfer_buffer_curr_len = length;
+}
+
+void init_record(){
+    // Sampling rate 16000Hz @ 16 bit resolution 
+    // This is hardcoded in the code. Don't change.
+    Audio.format(16000U, 16U);
+}
+
+void start_record(){
+  Audio.startRecord(recordCallback);
+}
+
+void prediction_callback(float *vec, int len){
+    int arg = argmax(vec, len);
+    int oldarg = *(int*)q_oldest(&votingQ);
+    if (oldarg >= NUM_LABELS || oldarg < 0)
+        oldarg = 0;
+    votingFrequence[arg]++;
+    votingFrequence[oldarg]--;
+    q_force_enqueue(&votingQ, &arg);
+    if (votingFrequence[arg] >= VOTE_MAJORITY){
+        char str[20];
+        sprintf(str, "Pred: %s (%d)", labelInvArr[arg], arg);
+        Screen.print(str, false);
+    }
+}
+
+
+void setup(){
+    q_init(&votingQ, votingContainer, VOTE_WIN_LEN, cb_write_int, cb_read_int);
+    votingFrequence[0] = 5;
+    Serial.begin(115200);
+    Screen.init();
+    delay(500);
+    initFastRNN0();
+    initFastRNN1();
+    initFC();
+    delay(500);
+    Screen.clean();
+    unsigned ret = sfastrnn2p_init(&fastrnnParams0,
+        &fastrnnParams1, &fcParams, prediction_callback);
+    Serial.printf("Return code: %d (init)\n", ret);
+    if(ret != 0)
+        error("Shallow FastRNN initialization failed (code %d)", ret);
+    if(ret != 0) while(1);
+    init_record();
+    delay(500);
+    Serial.println();
+    Serial.println("Ready");
+    Screen.print(0, "Ready");
+}
+
+int main(){
+    // Setup the predictor thread and the
+    // audio recording thread. The prediction
+    // thread has alrady started and is waiting for audio.
+    setup();
+    delay(500);
+    start_record();
+    while (1){
+        if (transfer_buffer_curr_len == 0){
+            // For a 16, 16 fastRNN model, this can be pushed
+            // 6ms without causing errors.
+            rtos:wait_ms(5);
+            continue;
+        }
+        unsigned ret = sfastrnn2p_add_new_samples(transfer_buffer,
+            transfer_buffer_curr_len);
+        if(ret != 0)
+            Serial.printf("Error pushing to interface %d\n", ret);
+        static int count = 0;
+        count += transfer_buffer_curr_len;
+        if(count % (128 * 1000) == 0)
+            Serial.printf("Pushed %d seconds\n", (count/16000));
+        transfer_buffer_curr_len = 0;
+    }
+}
+
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/src/model.h b/Applications/KeywordSpotting/MXChip-SRNN/test/src/model.h
new file mode 100644
index 000000000..8d70fbcef
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/src/model.h
@@ -0,0 +1,109 @@
+
+#include <sfastrnn.h>
+#include <fc.h>
+#include <arm_math.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+struct FastRNNParams fastrnnParams0;
+struct FastRNNParams fastrnnParams1;
+struct FCParams fcParams;
+
+void initFastRNN0();
+void initFastRNN1();
+void initFC();
+
+#ifdef __cplusplus
+}
+#endif
+static float combinedWMatrix0[] = {
+    -4.394342, 1.670318, -2.444137, 3.987555, -0.367528, 0.847272, -1.579511, -2.468433, -7.282849, -0.976210, 3.635147, 6.876060, -3.495753, -4.241759, 5.861881, -1.029778, -0.132852, 0.482839, -0.773421, -1.058756, -0.400583, 0.313920, 0.754386, 2.407314, 0.084614, -0.621101, -1.202959, -0.817329, 1.091781, 0.803296, -1.132104, -1.491276, -1.285186, -0.367634, -0.191333, 2.019341, 0.813238, 0.842223, 1.320992, 0.335768, -0.458256, 0.623994, -0.636713, 0.135170, 0.722895, -0.540891, 0.339657, -0.178645, 
+-5.010478, -12.386557, 3.541905, 1.687755, -0.226694, 2.467856, -2.713917, -4.092384, 8.001176, 1.380618, -3.027768, 1.509269, 1.249332, 4.262799, 1.940189, 0.479688, -0.647341, -0.274383, 0.621559, 0.840944, 0.471705, 1.386905, 2.151344, 1.836882, 0.543310, 0.328246, -0.259154, -0.031627, 0.408222, -0.686999, -0.324494, 0.529456, -0.041894, 0.162293, 0.634429, 0.658714, 0.309362, 0.206179, 0.323115, -0.444688, 0.045480, 0.334993, -0.038282, -0.554810, 0.144400, -0.221486, 0.129975, -0.449101, 
+-3.118361, -1.812039, -4.206928, 2.821902, -0.748216, 4.338758, -3.047090, -10.049188, 0.120711, -8.117956, 2.690513, -1.699157, 0.425068, 2.030299, -2.204330, -1.336558, -0.831021, -0.696517, 1.643283, 0.811341, 0.816283, 0.673865, -1.026075, -1.222155, -0.589451, -1.283914, -1.704754, -0.735262, 0.259212, 1.528514, 1.482427, 1.216248, 1.072276, 0.873297, 0.285459, -0.135662, 1.117006, -0.472917, 0.991429, -0.119520, 0.087565, -0.214628, -0.061496, -0.169957, 0.019995, -0.054380, -0.090222, -0.428023, 
+-0.242790, 1.813288, 0.119135, -2.969033, -1.675614, -3.901589, -0.091110, -7.544526, 1.960290, -0.037544, -0.672174, 4.773893, 0.150768, -3.041260, 3.332448, -0.244760, 0.657012, -2.135938, 2.968000, -1.933262, -0.587828, -1.612573, -1.273521, -1.634842, 1.549491, 2.493810, 3.421001, 2.132683, -0.197654, -0.376506, -1.291105, 0.537511, -0.426619, -1.155925, -0.591313, -1.176670, 1.275704, 0.069142, -1.310604, 0.469024, 0.390803, 0.219441, -0.309057, 0.795271, -0.377698, -0.204987, 0.098430, -0.072286, 
+1.659412, -0.136527, 0.939389, -3.545981, -3.184775, -2.974060, -0.538432, -3.639727, -0.837568, -1.604512, -0.579743, -4.922958, 1.545775, 0.529332, -7.399548, 4.370611, -0.080237, 1.754189, -0.956228, 0.022387, -1.777416, -0.993705, -2.662503, -1.556300, 1.677594, 1.765419, 2.185814, 2.665101, -1.390469, -0.851602, 0.933666, 1.104553, 2.189019, -0.020495, 1.062610, 0.149326, -1.536042, -2.015504, -0.590997, -0.187154, -0.888261, -0.192437, -0.007773, -0.195747, 0.024274, 0.260669, -0.274831, -0.121948, 
+-0.644862, 3.412475, -5.739103, 0.730205, 1.893141, -5.632226, -0.136634, -3.698142, 1.016315, -0.203452, 1.990294, -4.053802, -2.250560, 2.936410, 0.938369, -4.563478, -0.198095, 1.321754, -1.213177, 0.746386, 0.395729, 0.735425, 0.674619, -0.286715, -0.213787, 0.695955, 1.095577, 2.195754, 0.552678, 0.022346, 0.491991, -1.117851, -2.112240, -1.116424, -0.688602, -0.407817, -0.537197, 0.696354, 0.902197, 0.448935, -0.177437, -0.303541, 0.401238, -0.163165, -0.310976, 0.233415, -0.507773, 0.439906, 
+0.405050, 1.563615, -2.845270, 1.864122, 2.324951, -2.308045, 1.342459, -0.484539, 0.426602, 1.209528, -1.047912, -0.280595, -0.585254, 1.265471, -2.105597, -6.096662, -0.203713, -0.298388, 1.028528, 0.607156, 0.308826, -0.618201, -0.722835, -1.619279, -0.824562, -0.617465, -0.543220, -1.233350, -2.441530, -1.802619, 2.627425, 2.585896, 1.069127, 1.195297, -1.627771, 0.595738, 1.037760, 0.417393, -0.090837, -0.146070, -0.176088, 1.056672, -0.459774, 0.658548, -0.328070, -0.268748, -0.390144, 0.451860, 
+-1.733371, -2.615444, -3.020136, -1.453281, 2.591630, -2.385435, -0.073184, -3.689105, -2.830683, -0.677763, 2.251215, 1.987840, -0.602462, -6.297771, 1.119586, -0.775122, -0.517076, -1.090677, 2.195573, 3.490596, 3.532367, 1.302304, -0.036746, -2.973045, -0.614896, -1.700183, -0.836986, -0.260505, -0.024295, 0.439021, -0.761616, -1.531463, -1.656251, 0.141997, -0.249461, 1.028342, 1.568828, 0.854612, 0.420676, 0.125925, -0.075899, 0.404789, 0.054414, -0.723230, 0.321348, -0.352063, -0.073613, 0.100152, 
+-0.264005, -1.951415, -1.429368, -1.594596, -0.371441, 0.040082, 1.656364, 2.827784, -8.510094, -3.045576, 0.982709, -0.357535, 0.458934, -5.671675, 2.810453, -2.509865, -0.055260, 0.149244, -2.556470, -0.289469, -0.956199, -0.967695, -1.253180, 0.375230, 0.470758, -0.480561, -1.188382, -0.385000, 0.729667, 1.763392, 0.788664, -0.381417, -0.188639, 0.700852, -0.828685, -0.422568, -0.385014, -0.557915, -0.905237, -0.235940, 0.497495, 0.208721, 0.115759, 0.290364, 0.086713, -0.006929, -0.818390, 1.248167, 
+-2.651732, -0.196781, 2.481395, -1.427539, -3.650820, -4.447654, 3.010999, -2.660051, 0.965630, -7.510485, -3.075092, -0.512343, 0.263273, 3.732776, -0.218930, -3.831048, 0.297317, -1.412834, -1.033311, 0.403555, 1.724447, 2.214988, 3.159698, 0.195241, -2.543757, -1.156173, -0.658126, -0.196266, -0.891090, -0.997414, -0.669083, -1.452945, 0.267728, 0.721529, 1.612172, 0.383177, 1.040189, 0.653729, 0.163864, -0.094382, 0.666856, 0.253287, 0.024207, -0.089818, -0.387213, -0.115701, 0.229131, -0.504887, 
+3.510151, 13.682715, -1.262339, -0.938372, 0.214310, -4.568920, 1.900012, 0.168166, -1.113414, 4.860772, -3.513642, -7.361302, 0.977453, 3.073361, 0.495730, 1.790086, 0.665338, 0.358030, 1.767592, 1.373327, -0.377032, -1.457430, -0.920559, 0.233391, 1.578097, 1.341954, 0.032383, 1.021583, 0.113483, -0.202995, 0.503583, 0.312998, -0.546984, 0.279893, 0.275056, -1.264586, 1.479502, -0.670943, 0.622271, -0.101685, 0.061433, -2.146494, -0.167393, -1.107415, -1.833909, -2.074111, -1.063813, -2.169270, 
+-2.608964, -4.021140, -0.128930, -4.680740, -3.001136, 1.165962, -2.217025, 1.472322, 1.831619, -2.566680, 2.990274, -9.275423, -1.245824, 3.842207, 0.191467, -2.173995, -0.083123, -0.301617, -1.096413, -0.374791, -1.169248, 0.193628, 0.578226, 0.233941, 0.187847, 0.837287, -0.367849, 0.188699, 1.307282, 2.631428, 3.713660, 2.160410, 1.891309, -0.735538, -1.449885, -1.196488, -0.918136, -0.692220, -1.113876, -0.364902, 0.377525, 0.318798, -0.076559, -0.037340, -0.291767, -0.562892, 0.066101, -0.191011, 
+1.705875, -2.202471, -0.187324, -0.836479, 0.115738, 4.280789, 0.098201, -0.026486, 3.779680, 0.790323, -0.340409, -1.200357, 0.258244, 3.573425, -2.039930, 0.448585, -0.263841, 1.054291, -0.734569, 0.357012, 0.344547, -0.227532, -1.293920, -2.773390, -0.967389, -0.291874, 1.814994, 1.863690, 2.086262, 2.177715, -0.059085, -0.779559, 0.034168, -1.790127, 0.128300, -0.917041, -0.207902, -0.319050, 0.061688, 1.112218, -0.369199, -0.069757, -0.304790, 0.515871, 0.195216, -0.047352, 0.470148, 0.135613, 
+-1.801733, -14.333292, -6.710084, 2.027344, -0.738840, 2.533691, -1.460382, 0.617199, 3.300643, -1.117942, 3.094809, 1.257551, -3.057455, -7.443308, 0.006572, -0.762965, -0.756610, 1.186737, -0.927841, -0.341335, -0.853035, 0.217751, 0.376304, 2.052606, 1.383751, 0.387989, 0.656082, 0.743186, -0.085105, -0.018542, 0.111765, -0.176987, 0.164592, -0.040699, -0.555956, 1.440574, -0.231119, 1.188938, 1.542780, -0.064015, -0.069175, 0.515627, 0.091999, -0.237204, 0.515334, -0.054009, 0.603516, -0.318698, 
+-2.384683, 2.217553, -0.510579, -3.762332, 0.927062, 1.052021, -0.594735, 2.678587, 5.472569, -5.223604, 0.411627, 0.326158, 3.073533, 0.891218, -7.836277, -0.877050, 0.337595, 0.027045, 0.207879, 1.107072, 2.751967, 3.531062, 1.655158, -3.068271, -3.484626, -1.509957, -0.649874, -0.190971, -1.526214, -1.772561, 0.418379, 0.525252, 0.049277, 1.424461, 0.954263, -0.375417, -1.519454, 0.281435, 0.668371, 0.240059, 0.549209, 0.566503, -0.102672, -0.965943, 0.562778, -0.635043, 1.523030, -0.806144, 
+-4.007565, -2.384225, 1.985205, 5.132339, -1.038108, -3.017178, 6.698817, 0.000970, -4.567681, -0.637183, -1.207071, 1.972803, -5.824776, 2.519549, 5.051178, -10.033369, -0.685569, -0.454371, -0.362847, -0.009798, 0.080245, -0.475046, -0.687661, 0.673480, 1.079727, 0.344966, -1.493761, -1.561329, 0.848099, 3.756004, 4.460750, 0.785049, -1.214222, -1.517126, -1.006831, -0.269839, 0.436658, 0.493897, -0.802526, -0.039728, 0.621665, -0.144493, -0.240633, 0.607524, -0.736513, 0.338355, -0.749308, 0.594335, 
+
+ };
+
+static float combinedBMatrix0[] = {-0.483399,-5.375202,-0.894719,-2.971153,0.176196,-4.627392,2.712877,-3.889010,3.451340,-0.512680,3.635937,-2.563429,-1.778029,-1.228812,-4.482069,-2.371642,};
+
+static float combinedWMatrix1[] = {
+    -8.993324, 5.766108, -0.984275, 1.232846, -0.784503, -1.215732, -1.109800, -1.419134, 0.867878, 0.985043, 1.606076, -0.275711, 1.211763, -0.110211, 2.418703, -2.073111, 1.863143, -4.919676, -2.105419, 3.112989, 3.771616, 3.573565, -0.141503, -1.749448, 2.866004, 1.695223, -2.021968, -1.316671, 1.155566, 2.337256, -0.808888, 0.022269, 
+1.040456, -5.227429, -0.590861, 2.846540, 2.510352, 8.778857, 0.772251, 0.013926, 1.175662, -0.919632, 1.295280, 3.194552, -0.671261, -2.404313, 2.420117, 0.925858, -1.226532, -0.982795, -2.969703, 0.164707, -0.217380, -0.768241, -0.376959, 2.921228, 3.840075, 2.656377, 0.184129, 4.891365, -0.248023, -1.842269, 2.994883, -0.866867, 
+-2.433946, 2.733418, -7.199495, 1.227334, -4.087968, -4.372028, 7.848134, -2.760289, 3.304864, -1.298907, 1.932202, -0.909907, 0.924740, -1.356102, -5.423501, -3.767486, 0.325634, -2.054650, 2.083872, 0.728384, 1.154159, 0.952869, 1.352677, 4.639591, -0.716756, 0.798336, -1.293890, 1.404328, -0.389740, -0.894149, 0.415447, -0.260267, 
+-0.407898, 0.768603, 1.095211, -4.719667, -3.602338, 3.734942, 1.431481, 2.184455, 0.390121, 1.675800, -0.604315, 0.434035, 1.974323, -1.010169, 0.466087, 0.810820, -3.064052, 1.466479, 0.240817, -1.555377, 1.680161, 1.640082, -2.491495, -3.465886, 0.882935, -0.745272, 3.704679, 4.501615, 0.154132, -2.079676, -0.336283, 1.146916, 
+1.872181, -0.171051, 0.428701, 0.277004, -6.850987, 0.018607, -1.209911, 5.147545, 2.395395, -0.063215, -3.504235, -0.782541, -1.775267, -1.429212, -1.090621, 3.328624, -0.369251, 0.046201, 0.682975, -1.618495, 1.281623, -2.842376, 0.478221, 2.351111, -2.736117, -1.092621, 1.578171, 4.780344, 0.432083, -0.790555, -2.146031, 0.992934, 
+2.229246, -3.632861, 2.121870, 1.937214, 0.773552, -7.277787, 1.350840, 3.131287, 0.261730, -2.937069, 1.273218, 2.050651, -2.149755, -3.658037, 1.927494, 4.298773, -0.149977, 3.697038, -0.205383, -2.551378, -0.561584, -2.299595, 1.620943, 0.698497, -0.871604, -1.842371, -2.693478, -1.622483, 2.097851, -5.380957, -2.524892, 0.540286, 
+1.974882, 0.860660, 0.483594, -4.428236, 0.254198, -0.738776, -7.166861, 4.724154, -0.094901, 3.128331, -0.223106, 0.047523, -1.512438, -0.349958, 1.349454, -1.073704, 0.963849, -7.747976, 2.641978, 1.012649, 0.867783, 3.279111, -1.728747, -1.165767, -1.853621, 2.364444, -0.712560, 1.917849, 0.494486, 1.950737, -0.027571, 0.073560, 
+0.185064, 2.787849, 1.392283, -3.992270, -2.176939, 1.571382, -3.505462, -10.164021, -1.369556, 1.395783, -2.141547, -5.636579, 3.930323, 0.386195, -6.129729, -1.326905, -0.328154, 1.842927, -4.650984, -1.256178, 1.218856, -2.946430, -0.695355, -0.041204, -2.726118, 2.575443, 1.594451, -1.125996, -0.521107, 1.731262, 1.239454, 1.648101, 
+-1.645357, 2.483098, 1.378480, -1.034285, 0.286449, 1.968899, -0.603014, 4.894711, -4.844470, 4.013346, -5.933038, 3.384772, 5.367784, 0.994224, -2.253595, -0.450002, 0.113909, -9.981983, 2.174092, 0.458702, -0.550130, 4.121709, 0.055792, 3.381026, 0.224217, -2.882266, -0.534291, 0.376481, 0.097206, -1.507773, 0.389125, -0.527342, 
+1.772734, -2.101506, 2.286718, 3.837304, -0.114564, -0.330263, 1.073511, 0.029877, -1.633782, -7.562314, -0.648720, 1.629470, 3.975489, 2.423227, 6.425655, 7.710009, 0.337786, -3.375684, -1.860515, 0.107860, 1.165393, -0.612101, 0.055045, -1.761673, 1.279403, 5.502572, -0.987779, 1.097176, 0.532789, -5.638363, 2.125039, 0.035663, 
+-1.731669, 1.573543, -1.810375, 2.018543, -4.017689, -2.031807, 2.143212, 0.437938, 2.074214, -2.458002, -7.663488, 2.675484, 7.721604, -3.649217, -3.664882, 4.330673, 0.434385, 3.215706, 1.291247, -1.357334, 1.918163, -1.578749, 0.887113, 0.056447, 4.543562, -2.219110, -0.812309, -1.380378, 0.706664, -1.992625, -0.605155, 1.648007, 
+-0.745284, -0.395060, 2.257222, -2.050334, -2.811226, -3.223480, -0.935113, 4.670177, -0.166676, 0.190502, 4.950702, -5.055352, -2.926551, -0.875040, -2.181215, 2.516552, 3.281529, 4.631181, 0.170389, -1.290764, -1.637012, 1.986015, 2.599300, -0.560773, -1.093340, -2.086372, -2.141530, 5.056557, -1.756402, -4.758149, -0.287663, -1.831596, 
+3.072729, -2.615700, 4.131379, -2.967680, 0.447018, 4.740140, -4.009865, 0.703566, -0.613143, 2.639772, -1.038132, 0.775154, -1.156957, 0.229450, 2.784570, -0.660779, 2.145897, 0.258853, -0.768564, -1.063183, 0.617708, -0.135477, -3.456913, 0.023365, 1.792191, 0.239879, 3.347177, -7.541625, 0.274615, -0.131922, -0.664101, 2.145448, 
+-1.414440, 1.015114, 1.871301, -1.742095, 1.538365, 1.129859, -0.626152, -0.138937, 1.232638, -1.846977, 5.286344, -2.819705, -3.333694, -6.735012, -0.789252, -1.318035, 0.881870, 1.963813, -2.723712, -1.828124, -0.737036, -2.770875, -1.962267, -6.279300, -2.201661, -5.280858, 0.328632, -4.216747, 2.882310, 0.202990, -1.582065, 6.267218, 
+-1.148481, -1.254931, 1.314193, 0.969432, 0.429214, -3.153016, -1.708260, 4.345199, -0.671442, 1.874414, 0.090322, -0.330771, -0.801841, 0.734000, -7.610411, 1.416348, 0.229478, 0.114050, -2.328229, 0.822486, -0.594291, 1.357512, -4.062139, -0.165713, -2.305657, 2.479547, 1.950884, -4.972633, 1.115902, -4.844807, 0.812774, 6.339092, 
+1.136031, 1.501339, -2.519958, 1.596872, 0.409924, -5.161640, -2.744563, -2.843511, -1.029676, -3.751853, 5.121879, -2.964035, -1.117965, 0.380782, -2.735341, -6.494120, -1.351529, -1.072190, 4.475897, -0.367306, -0.331998, 4.780028, 0.935141, 0.891659, -0.969988, 2.362436, -2.633953, 0.126173, 0.406457, 0.058628, -4.265975, -1.729679, 
+
+ };
+static float combinedBMatrix1[] = {0.390521,-1.568006,-1.247215,2.683117,-5.352313,1.015610,-2.181136,-2.871274,4.206901,2.940511,-0.876600,1.264730,2.177765,-0.583102,-1.096310,0.976021,};
+
+static float fcW[] = {1.153998, 2.908503, 2.931298, -6.591868, 8.570377, 1.777791, 4.249845, -5.324548, -1.933409, 1.652255, -3.008340, 2.561109, 5.091272, 2.375975, 0.114272, 1.825302, 
+4.079215, 1.132804, 1.410711, 3.920387, -2.375091, -3.254151, -0.664488, 1.958657, -3.064375, 1.514691, -4.174952, 4.311438, -3.373156, 5.219816, 7.911401, -6.436582, 
+2.892433, 0.543585, 0.357800, -1.411686, 5.022547, -2.542740, -4.708998, 3.959384, 5.811014, -6.125777, -3.734309, -1.885103, 1.591531, 8.301410, 4.805660, -5.946973, 
+-8.308530, 2.225836, 2.609653, 1.061591, -1.923773, -0.394133, 2.562978, -13.416260, 0.970302, -4.682763, 2.798755, -4.203907, 3.902050, 0.464174, -0.507031, 9.735559, 
+2.582557, 4.024098, -3.741822, 1.387635, -0.826144, -4.692614, -2.862955, -8.454850, -4.566405, -9.172646, 7.517973, -2.362217, 1.423986, 12.640054, 0.416312, -1.751263, 
+-2.243015, -5.913934, -0.829215, 4.400629, -6.072413, -0.354700, -3.499763, 3.878453, -0.876954, -1.165360, 0.531110, 5.968567, 4.987993, 1.700330, -9.198635, -6.721683, 
+4.435347, -6.062106, -4.835220, -3.835865, 12.558668, -4.110910, -5.846931, 4.634123, -0.288808, 10.246997, 3.454742, -2.433312, -2.960399, -10.358557, -8.287789, 3.800376, 
+-3.147445, -1.744314, 1.603617, 7.252875, -7.243377, -3.880891, 12.423599, 5.735993, -3.459105, 2.935940, 4.733056, -4.282575, -7.891159, 4.837924, 3.405554, -2.671608, 
+3.892418, -1.989835, 0.918071, 0.043651, -7.037201, 10.196301, 3.654709, -10.874338, -1.109066, -7.223512, 4.990910, -5.295347, 2.799649, -0.427754, 2.969462, 1.733097, 
+-8.279483, 1.865549, 3.969562, 4.548631, -0.153620, 4.558271, 10.215759, -1.037572, 4.205336, -7.001508, -3.296622, -1.629840, 5.132443, -9.705027, -2.475006, 5.934279, 
+-1.256917, 2.953662, -3.081605, 1.836611, -2.805650, 4.750621, -0.547419, 5.925297, 3.857441, 7.218900, -3.965770, 7.754599, -11.122416, -3.871731, -6.957188, -3.133772, 
+-4.082258, 6.178590, -0.460052, -0.987127, 2.318068, -8.007840, -7.212764, 4.920698, -1.504554, 9.524718, -4.809536, -1.719856, -2.680660, -8.458871, 9.215178, 7.220995, 
+9.246867, -5.056426, -0.473073, -11.486769, 4.229325, 8.818913, -1.797206, 8.909225, 7.959111, 1.329669, -2.135564, 0.937130, 1.458388, -4.138429, -2.824711, -4.755872, 
+};
+static float fcB[] = {3.389326,-3.066761,0.569489,2.105324,-0.605301,2.095950,-3.786515,0.042079,-2.827516,-0.433185,2.729823,-2.976138,-2.544198,};
+
+void initFastRNN0() {
+    fastrnnParams0.timeSteps = 8;
+    fastrnnParams0.featLen = 32;
+    fastrnnParams0.statesLen = 16;
+    fastrnnParams0.W = combinedWMatrix0;
+    fastrnnParams0.b = combinedBMatrix0;
+    fastrnnParams0.alpha = 0.371637;
+    fastrnnParams0.beta = 0.892619;}
+
+void initFastRNN1() {
+    fastrnnParams1.timeSteps = 6;
+    fastrnnParams1.featLen = 16;
+    fastrnnParams1.statesLen = 16;
+    fastrnnParams1.W = combinedWMatrix1;
+    fastrnnParams1.b = combinedBMatrix1;
+    fastrnnParams1.alpha = 0.393828;
+    fastrnnParams1.beta = 0.968985;
+}
+
+void initFC(){
+    fcParams.W = fcW;
+    fcParams.B = fcB;
+    fcParams.inputDim = 16;
+    fcParams.outputDim = 13;
+}
+
+
+float featNormMean[] = {3.558071,5.307965,6.723956,6.980298,7.084899,7.346985,7.488606,7.989687,7.914260,7.832272,7.907389,7.931001,7.815156,7.930761,8.122243,8.164267,8.300410,8.296384,8.287304,8.508408,8.552395,8.582291,8.675160,8.630129,8.566705,8.476798,8.394355,8.335714,8.255317,8.179764,7.816524,6.466621,};
+float featNormStd[] = {5.316042, 5.429762, 5.761431, 5.828824, 5.859360, 6.028826, 6.098757, 6.153158, 6.109560, 5.981027, 5.866415, 5.777138, 5.708584, 5.648618, 5.630316, 5.599686, 5.539146, 5.473228, 5.465712, 5.505444, 5.488520, 5.451288, 5.437470, 5.387079, 5.305981, 5.239823, 5.195953, 5.158002, 5.121702, 5.103227, 5.064711, 4.885242, };
+const char *labelInvArr[] = {"Noise", "go","no","on","up","bed","cat","dog","off","one","six","two","yes",};
+
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/src/params.zip b/Applications/KeywordSpotting/MXChip-SRNN/test/src/params.zip
new file mode 100644
index 000000000..15cb0ecb3
Binary files /dev/null and b/Applications/KeywordSpotting/MXChip-SRNN/test/src/params.zip differ
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/src/template_MXChip.py b/Applications/KeywordSpotting/MXChip-SRNN/test/src/template_MXChip.py
new file mode 100644
index 000000000..f9e61dfd0
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/src/template_MXChip.py
@@ -0,0 +1,140 @@
+def getTemplate(W0, B0, alpha0, beta0, W1, B1, alpha1, beta1, timeSteps0,
+                timeSteps1, fcW, fcB, numOutput, labelMap, normalize=False,
+                mean=None, std=None):
+    statesLen0 = len(B0)
+    featLen0 = W0.shape[1] - statesLen0
+    statesLen1 = len(B1)
+    featLen1 = W1.shape[1] - statesLen1
+    assert W0.shape[0] == statesLen0
+    assert W1.shape[0] == statesLen1
+    assert featLen1 == statesLen0
+    assert fcW.shape[0] == numOutput
+    assert fcW.shape[1] == statesLen1
+    assert fcW.ndim == 2
+    assert fcB.ndim == 1
+    assert fcB.shape[0] == numOutput
+
+    W0Str = ''
+    for i in range(W0.shape[0]):
+        for j in range(W0.shape[1]):
+            W0Str += '%f, ' % W0[i][j]
+        W0Str += '\n'
+
+    B0Str = ''
+    for i in range(B0.shape[0]):
+        B0Str += '%f,' % B0[i]
+
+    W1Str = ''
+    for i in range(W1.shape[0]):
+        for j in range(W1.shape[1]):
+            W1Str += '%f, ' % W1[i][j]
+        W1Str += '\n'
+
+    B1Str = ''
+    for i in range(B1.shape[0]):
+        B1Str += '%f,' % B1[i]
+
+    FCWStr = ''
+    for i in range(fcW.shape[0]):
+        for j in range(fcW.shape[1]):
+            FCWStr += '%f, ' % fcW[i][j]
+        FCWStr += '\n'
+
+    FCBStr = ''
+    for i in range(fcB.shape[0]):
+        FCBStr += '%f,' % fcB[i]
+
+    retStr = '''
+#include <sfastrnn.h>
+#include <fc.h>
+#include <arm_math.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+struct FastRNNParams fastrnnParams0;
+struct FastRNNParams fastrnnParams1;
+struct FCParams fcParams;
+
+void initFastRNN0();
+void initFastRNN1();
+void initFC();
+
+#ifdef __cplusplus
+}
+#endif
+static float combinedWMatrix0[] = {
+    %s
+ };
+
+static float combinedBMatrix0[] = {%s};
+
+static float combinedWMatrix1[] = {
+    %s
+ };
+static float combinedBMatrix1[] = {%s};
+
+static float fcW[] = {%s};
+static float fcB[] = {%s};
+
+void initFastRNN0() {
+    fastrnnParams0.timeSteps = %d;
+    fastrnnParams0.featLen = %d;
+    fastrnnParams0.statesLen = %d;
+    fastrnnParams0.W = combinedWMatrix0;
+    fastrnnParams0.b = combinedBMatrix0;
+    fastrnnParams0.alpha = %f;
+    fastrnnParams0.beta = %f;}
+
+void initFastRNN1() {
+    fastrnnParams1.timeSteps = %d;
+    fastrnnParams1.featLen = %d;
+    fastrnnParams1.statesLen = %d;
+    fastrnnParams1.W = combinedWMatrix1;
+    fastrnnParams1.b = combinedBMatrix1;
+    fastrnnParams1.alpha = %f;
+    fastrnnParams1.beta = %f;
+}
+
+void initFC(){
+    fcParams.W = fcW;
+    fcParams.B = fcB;
+    fcParams.inputDim = %d;
+    fcParams.outputDim = %d;
+}
+
+''' % (W0Str, B0Str, W1Str, B1Str, FCWStr, FCBStr, timeSteps0, featLen0,
+       statesLen0, alpha0, beta0, timeSteps1, featLen1, statesLen1, alpha1,
+       beta1, statesLen1, numOutput)
+
+    if normalize == True:
+        meanStr = ''
+        for val in mean:
+            meanStr += '%f,' % val
+        stdStr = ''
+        for val in std:
+            stdStr += '%f, ' % val
+        str2 = '''
+float featNormMean[] = {%s};
+float featNormStd[] = {%s};
+''' % (meanStr, stdStr)
+        retStr += str2
+    label_inv = {}
+    for key in labelMap:
+        val = labelMap[key]
+        if val in label_inv:
+            label_inv[val].append(key)
+        else:
+            label_inv[val] = [key]
+    assert len(label_inv) == numOutput
+    for i in range(1, numOutput):
+        assert len(label_inv[i]) == 1
+    
+    label_inv_str = 'const char *labelInvArr[] = {"Noise", '
+    for i in range(1, numOutput):
+        label_inv_str += '"%s",' % label_inv[i][0];
+    label_inv_str += "};\n"
+    retStr += label_inv_str;
+    return retStr
+
+
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/test_fastrnn/fastrnntest.py b/Applications/KeywordSpotting/MXChip-SRNN/test/test_fastrnn/fastrnntest.py
new file mode 100644
index 000000000..374b0d0a7
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/test_fastrnn/fastrnntest.py
@@ -0,0 +1,61 @@
+import numpy as np
+
+def sigmoid(x):
+    return 1.0 / (1 + np.exp(-x).astype(np.float32))
+
+class FastRNN:
+    def __init__(self):
+        self.timesteps = 8
+        self.featLen = 6
+        self.statesLen = 4
+        self.alpha = 0.2
+        self.beta = 0.8
+        W = [ 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0,
+            0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1,
+            0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2,
+            0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3,]
+        B = [1, 2, 3, 4,]
+        W = np.array(W)
+        self.W = np.reshape(W, [self.statesLen, self.statesLen + self.featLen])
+        self.B = np.array(B)
+
+    def cell(self, x, h):
+        W = self.W
+        B = self.B
+        hx = np.concatenate([h, x])
+        hcomb = np.matmul(W, hx)
+        h_ = hcomb  + B
+        h_ = sigmoid(h_)
+        h_ = self.alpha * h_ + self.beta * h
+        return h_
+
+    def unroll(self, x_list):
+        h = np.zeros(self.statesLen)
+        for x in x_list:
+            h = self.cell(x, h)
+        return h
+
+
+def main():
+    fastrnn = FastRNN()
+    x = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6])
+    h0 = np.zeros(fastrnn.statesLen)
+    h = fastrnn.cell(x, h0)
+    print(h)
+    xx = [
+        0.0,0.01,0.02,0.03,0.04,0.05,
+        0.06,0.07,0.08,0.09,0.1,0.11,
+        0.12,0.13,0.14,0.15,0.16,0.17,
+        0.18,0.19,0.2,0.21,0.22,0.23,
+        0.24,0.25,0.26,0.27,0.28,0.29,
+        0.3,0.31,0.32,0.33,0.34,0.35,
+        0.36,0.37,0.38,0.39,0.4,0.41,
+        0.42,0.43,0.44,0.45,0.46,0.47
+    ]
+    xx = np.reshape(xx, [-1, fastrnn.featLen])
+    h = fastrnn.unroll(xx)
+    print(h)
+
+main()
+
+
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/test_fastrnn/test_fastrnn.cpp b/Applications/KeywordSpotting/MXChip-SRNN/test/test_fastrnn/test_fastrnn.cpp
new file mode 100644
index 000000000..746462d88
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/test_fastrnn/test_fastrnn.cpp
@@ -0,0 +1,97 @@
+#include "fastrnn.h"
+#include <Arduino.h>
+#include <OledDisplay.h>
+
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+struct FastRNNParams fastrnnParams_test;
+void initFastRNN_test();
+#ifdef __cplusplus
+}
+#endif
+static float combinedWMatrix[] = {
+    0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0,
+    0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1,
+    0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2,
+    0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3,
+ };
+
+static float combinedBMatrix[] = {1, 2, 3, 4};
+
+
+void initFastRNN_test() {
+    fastrnnParams_test.timeSteps = 8;
+    fastrnnParams_test.featLen = 6;
+    fastrnnParams_test.statesLen = 4;
+    fastrnnParams_test.W = combinedWMatrix;
+    fastrnnParams_test.b = combinedBMatrix;
+    fastrnnParams_test.alpha = 0.2;
+    fastrnnParams_test.beta = 0.8;
+}
+
+unsigned verifyOutput(float *result, float *expected, unsigned length){
+    unsigned errorCount = 0;
+    for (int i = 0; i < length; i++){
+        if(abs(result[i] - expected[i]) > 0.00001)
+            errorCount += 1;
+    }
+    return errorCount;
+}
+
+unsigned testFastRNN(){
+    unsigned errorCode = 0;
+    initFastRNN_test();
+    int statesLen = fastrnnParams_test.statesLen;
+    int featLen = fastrnnParams_test.featLen;
+
+    float x[] = {0.1, 0.2, 0.3, 0.4, 0.5, 0.6};
+    float input_h[] = {0.0, 0.0, 0.0, 0.0};
+    float hx[statesLen + featLen];
+    float hx_expected[] = {0.0, 0.0, 0.0, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6};
+    combineHX(&fastrnnParams_test, input_h, x, hx);
+    if (verifyOutput(hx, hx_expected, statesLen + featLen))
+        errorCode |= 1;
+    float result_h[statesLen];
+    FastRNNStep(&fastrnnParams_test, x, input_h, result_h);
+    float result_h_expected[] = {0.18798266, 0.19625871, 0.19886951, 0.19966154};
+    if (verifyOutput(result_h, result_h_expected, statesLen))
+        errorCode |= 2;
+
+    float xx[] = {
+        0.0,0.01,0.02,0.03,0.04,0.05,
+        0.06,0.07,0.08,0.09,0.1,0.11,
+        0.12,0.13,0.14,0.15,0.16,0.17,
+        0.18,0.19,0.2,0.21,0.22,0.23,
+        0.24,0.25,0.26,0.27,0.28,0.29,
+        0.3,0.31,0.32,0.33,0.34,0.35,
+        0.36,0.37,0.38,0.39,0.4,0.41,
+        0.42,0.43,0.44,0.45,0.46,0.47
+    };
+    FastRNNInference(&fastrnnParams_test, xx,  result_h);
+    float result_h_expected2[] = {0.77809181, 0.81525842, 0.82698329, 0.83059075};
+    if (verifyOutput(result_h, result_h_expected2, statesLen))
+        errorCode |= 3;
+    return errorCode;
+}
+
+void setup(){
+    Serial.begin(115200);
+    Screen.init();
+    delay(500);
+    Serial.println("Ready");
+}
+
+void loop(){
+    unsigned errorCode = testFastRNN();
+    Serial.printf("FastRNN error Code: %d\n", errorCode);
+    delay(400);
+}
+
+
+int main(){
+    setup();
+    for(int i=0; i < 100; i++)
+        loop();
+}
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/test_fc/test_fc.cpp b/Applications/KeywordSpotting/MXChip-SRNN/test/test_fc/test_fc.cpp
new file mode 100644
index 000000000..370319403
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/test_fc/test_fc.cpp
@@ -0,0 +1,103 @@
+#include <OledDisplay.h>
+#include <Arduino.h>
+#include "fc.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif 
+struct FCParams fcParams_test;
+extern void initFC_test();
+#ifdef __cplusplus
+}
+#endif
+
+static float W[] = {
+        0.496714,-0.138264,0.647689,1.523030,
+        -0.234153,-0.234137,1.579213,0.767435,
+        -0.469474,0.542560,-0.463418,-0.465730,
+};
+static float B[] = {0.241962,-1.913280,-1.724918,};
+static unsigned inputDim = 4;
+static unsigned outputDim = 3;
+
+void initFC_test(){
+    fcParams_test.W = W;
+    fcParams_test.B = B;
+    fcParams_test.inputDim = inputDim;
+    fcParams_test.outputDim = outputDim;
+}
+
+unsigned runFCTests(){
+	float epsilon7 = 1e-4f;
+	float vec_0[] = {-0.562288,-1.012831,0.314247,-0.908024,};
+	float vec_1[] = {-1.412304,1.465649,-0.225776,0.067528,};
+	float vec_2[] = {-1.424748,-0.544383,0.110923,-1.150994,};
+	float vec_3[] = {0.375698,-0.600639,-0.291694,-0.601707,};
+	float vec_4[] = {1.852278,-0.013497,-1.057711,0.822545,};
+	float res_0[] = {-1.076709,-1.745063,-1.733194,};
+	float res_1[] = {-0.705581,-2.230472,-0.193496,};
+	float res_2[] = {-2.071616,-2.160353,-0.866747,};
+	float res_3[] = {-0.593720,-2.783037,-1.811772,};
+	float res_4[] = {1.731574,-3.382938,-2.494760,};
+	unsigned testFailures = 0;
+	unsigned __inpDim = 4;
+	unsigned __outputDim = 3;
+	unsigned nonLinearity = 0;
+
+	initFC_test();
+
+	float result[__outputDim];
+	FCInference(&fcParams_test, vec_0, result, nonLinearity);
+	for(int i = 0; i < __outputDim; i++)
+		if ((fabs(res_0[i] - result[i])) > epsilon7)
+			testFailures |= 2;
+
+	FCInference(&fcParams_test, vec_1, result, nonLinearity);
+	for(int i = 0; i < __outputDim; i++)
+		if ((fabs(res_1[i] - result[i])) > epsilon7)
+			testFailures |= 4;
+	
+	FCInference(&fcParams_test, vec_2, result, nonLinearity);
+	for(int i = 0; i < __outputDim; i++)
+		if ((fabs(res_2[i] - result[i])) > epsilon7)
+			testFailures |= 8;
+	
+	FCInference(&fcParams_test, vec_3, result, nonLinearity);
+	for(int i = 0; i < __outputDim; i++)
+		if ((fabs(res_3[i] - result[i])) > epsilon7)
+			testFailures |= 16;
+	
+	FCInference(&fcParams_test, vec_4, result, nonLinearity);
+	for(int i = 0; i < __outputDim; i++)
+		if ((fabs(res_4[i] - result[i])) > epsilon7)
+			testFailures |= 32;
+	return testFailures;
+}
+
+void setup(){
+    Screen.init();
+    Serial.begin(115200);
+}
+
+void loop(){
+    // print a string to the screen with wrapped = false
+    Screen.print("Hello my dude", false);
+    delay(1000);
+    int a = runFCTests();
+    // print a string to the screen with wrapped = true
+	char buf[100];
+    sprintf(buf, "Testing FC\nError Code: %d", a);
+    Screen.print(buf);
+	Serial.println(buf);
+    delay(3000);
+    // Clean up the screen
+    Screen.clean();
+    delay(1000);
+}
+
+int main(){
+	setup();
+	delay(500);
+	for(int i = 0; i < 100; i++)
+		loop();
+}
\ No newline at end of file
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/test_lstm/test_lstm.cpp b/Applications/KeywordSpotting/MXChip-SRNN/test/test_lstm/test_lstm.cpp
new file mode 100644
index 000000000..14f34db9d
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/test_lstm/test_lstm.cpp
@@ -0,0 +1,195 @@
+#include <OledDisplay.h>
+#include <Arduino.h>
+#include "lstm.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+struct LSTMParams lstmParams_test;
+void initLSTM_test();
+#ifdef __cplusplus
+}
+#endif
+static float combinedWMatrix[] = {
+    0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0,
+    0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1,
+    0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2,
+    0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3,
+    0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4,
+    0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5,
+    0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6,
+    0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7,
+    0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8,
+    1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9,
+    1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0,
+    1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1,
+    1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2,
+    1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2, 2.3,
+    1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2, 2.3, 2.4,
+    1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5,
+ };
+
+static float combinedBMatrix[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
+    11, 12, 13, 14, 15, 16};
+
+void initLSTM_test() {
+    lstmParams_test.timeSteps = 8;
+    lstmParams_test.featLen = 6;
+    lstmParams_test.statesLen = 4;
+    lstmParams_test.forgetBias = 1.0;
+    lstmParams_test.W = combinedWMatrix;
+    lstmParams_test.B = combinedBMatrix;
+}
+
+unsigned runLSTMTests(){
+	float epsilon7 = 1e-7f;
+	float epsilon5 = 1e-5f;
+	float epsilon6 = 1e-6f;
+	float epsilon4 = 1e-4f;
+
+	unsigned testFailures = 0;
+	initLSTM_test();
+	unsigned m = 4 * lstmParams_test.statesLen;
+	unsigned n = lstmParams_test.statesLen + lstmParams_test.featLen;
+	for(int i = 0; i < m; i++){
+		for (int j = 0; j < n; j++){
+			if (fabs(lstmParams_test.W[i * n + j] - 0.1*(float)(i + j + 1.0f)) >= epsilon4){
+				testFailures |= 2;
+			}
+		}
+	}
+
+	for(int i = 0; i < n; i++)
+		if ((fabs(lstmParams_test.B[i] -  (i + 1))) >= epsilon7)
+			testFailures |= 4;
+
+	float x[8][6] = {
+		{0.00f, 0.10f, 0.20f, 0.30f, 0.40f, 0.50f,},
+		{0.10f, 0.20f, 0.30f, 0.40f, 0.50f, 0.60f,},
+		{0.20f, 0.30f, 0.40f, 0.50f, 0.60f, 0.70f,},
+		{0.30f, 0.40f, 0.50f, 0.60f, 0.70f, 0.80f,},
+		{0.40f, 0.50f, 0.60f, 0.70f, 0.80f, 0.90f,},
+		{0.50f, 0.60f, 0.70f, 0.80f, 0.90f, 1.00f,},
+		{0.60f, 0.70f, 0.80f, 0.90f, 1.00f, 1.10f,},
+		{0.70f, 0.80f, 0.90f, 1.00f, 1.10f, 1.20f,},
+	};
+
+	float h[4] = {0.1f, 0.3f, 0.5f, 0.7f};
+	float dst[lstmParams_test.featLen + lstmParams_test.statesLen];
+	combineXH(&lstmParams_test, x[0], h, dst);
+	float target[] = {0.0f, 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.1f, 0.3f, 0.5f, 0.7f};
+	for(int i = 0; i < lstmParams_test.featLen + lstmParams_test.statesLen; i++){
+		if(fabs(target[i] - dst[i]) >= epsilon7)
+			testFailures |= 8;
+	}
+	float combinedOut[4 * lstmParams_test.statesLen];
+	matrixVectorMul(lstmParams_test.W, 4*lstmParams_test.statesLen,
+		lstmParams_test.statesLen + lstmParams_test.featLen, 
+		dst, combinedOut);	
+	float target2[] = {
+		2.1600000858f, 2.4700000286f, 2.7800002098f,
+		3.0900001526f, 3.4000000954f, 3.7100000381f,
+		4.0199999809f, 4.3299999237f, 4.6399998665f,
+		4.9499998093f, 5.2600002289f, 5.5699996948f,
+		5.8800001144f, 6.1900000572f, 6.5000000000f,
+		6.8099999428f
+	};
+	for(int i = 0; i < 4 * lstmParams_test.statesLen; i++){
+		if (fabs(target2[i] - combinedOut[i]) >= epsilon6){
+			testFailures |= 16;
+		}
+	}
+
+	float testVec0[] = {0.96f, 0.01f, 0.01f, 0.02f};
+	float testVec1[] = {0.02f, 0.94f, 0.02f, 0.02f};
+	float target3[] =  {0.98f, 0.95f, 0.03f, 0.04f};
+	vectorVectorAdd(testVec1, testVec0, 4);
+	for(int i = 0; i < 4; i++)
+		if(fabs(testVec1[i] - target3[i]) >= epsilon7)
+			testFailures |= 32;
+
+	float testVec2[] = {-0.2f, 0.2f, 0.0f, 1.2f, -1.2f};
+	vsigmoid(testVec2, 5);
+	float target4[] = {0.450166f, 0.549834f, 0.5f, 0.76852478f, 0.23147522f};
+	for(int i = 0; i < 5; i++){
+		if(fabs(testVec2[i] - target4[i]) >= epsilon7)
+			testFailures |= 64;
+	}
+
+	float testVec3[] = {-2.0f, 0.1f, 0.0f, 1.2f, -1.2f};
+	float target5[] = {-0.96402758f, 0.09966799f, 0.00f, 0.83365461f, -0.83365461f};
+	vtanh(testVec3, 5);
+	for(int i = 0; i < 5; i++){
+		if(fabs(testVec3[i] - target5[i]) >= epsilon7)
+			testFailures |= 128;
+	}
+
+	float result_c_h_o[3 * lstmParams_test.statesLen];
+	for(int i = 0; i < 3 * lstmParams_test.statesLen; i++){
+		result_c_h_o[i] = 0;
+	}
+	float target7[] = {
+		0.8455291f, 0.94531806f, 0.9820137f, 0.99423402f,
+		0.68872638f, 0.73765609f, 0.7539363f, 0.75916194,
+		0.99999976f,  1.0f, 1.0f, 1.0f};	
+
+	LSTMStep(&lstmParams_test, (float*)&(x[0]), result_c_h_o, result_c_h_o);
+	for(int i =0; i < 3 * lstmParams_test.statesLen; i++){
+		if(fabs(result_c_h_o[i] - target7[i]) >= epsilon7){
+			testFailures |= 256;
+			Serial.printf("%d %2.10f %2.10f\n", i, result_c_h_o[i], target7[i]);
+		}
+	}
+	float target8[] = {
+		1.8336372f, 1.94265039f, 1.98141956f, 1.99410193f,
+		0.95018068f, 0.95974363f, 0.96269107f, 0.9636085f,
+		1.0f, 1.0f, 1.0f, 1.0f};
+
+	LSTMStep(&lstmParams_test, (float*)&(x[1]), result_c_h_o, result_c_h_o);
+	for(int i =0; i < 3 * lstmParams_test.statesLen; i++){
+		if(fabs(result_c_h_o[i] - target8[i]) >= epsilon5){
+			testFailures |= 512;
+		}
+	}
+	float target9[] = {
+		7.81787791f, 7.93995698f, 7.98095536f,
+		7.99402111f, 0.99999968f, 0.99999975f,
+		0.99999977f, 0.99999977f, 1.0f,
+		1.0f, 1.0f, 1.0f};
+	for(int i = 2; i < 8; i++)
+		LSTMStep(&lstmParams_test, (float*)&(x[i]), result_c_h_o, result_c_h_o);
+	for(int i =0; i < 3 * lstmParams_test.statesLen; i++){
+		if(fabs(result_c_h_o[i] - target9[i]) >= epsilon5){
+			testFailures |= 1024;
+		}
+	}
+	return testFailures;
+}
+
+void setup(){
+    Screen.init();
+    Serial.begin(115200);
+}
+
+void loop(){
+    // print a string to the screen with wrapped = false
+    Screen.print("Hello my dude", false);
+    delay(1000);
+    int a = runLSTMTests(); 
+    // print a string to the screen with wrapped = true
+	Screen.print("Testing LSTM", false);
+	char buff[30];
+	sprintf(buff, "Error Code: %d", a);
+	Screen.print(1, buff);
+	Serial.println(buff);
+    delay(1000);
+    // Clean up the screen
+    Screen.clean();
+    delay(1000);
+}
+
+int main(){
+	setup();
+	for(int i = 0; i < 100; i++)
+		loop();
+}
\ No newline at end of file
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/test_mfcc/data.h b/Applications/KeywordSpotting/MXChip-SRNN/test/test_mfcc/data.h
new file mode 100644
index 000000000..17e0c20a4
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/test_mfcc/data.h
@@ -0,0 +1,81 @@
+
+static int32_t inputData[512] = {49, -13, 64, 152, -23, -23, 157, 76, -46, 54, 
+-46, -46, 24, -191, -172, -56, -101, 31, -90, -141, 
+146, -22, 6, -142, -54, 11, -115, 37, -60, -29, 
+-60, 185, -1, -105, 82, -122, 20, -195, -132, 19, 
+73, 17, -11, -30, -147, -71, -46, 105, 34, -176, 
+32, -38, -67, 61, 103, 93, -83, -30, 33, 97, 
+-47, -18, -110, -119, 81, 135, -7, 100, 36, -64, 
+36, 153, -3, 156, -261, 82, 8, -29, 9, -198, 
+-21, 35, 147, -51, -80, -50, 91, 32, -52, 51, 
+9, 96, -70, -32, -39, -146, 29, 26, 0, -23, 
+-141, -42, -34, -80, -16, 40, 188, 17, 25, -7, 
+-191, -2, 6, 246, -19, 30, -3, -116, 114, 75, 
+79, -90, 140, -140, 58, 219, -99, -56, 9, -50, 
+-155, 6, -106, 47, -91, 154, -78, -32, 81, -123, 
+22, 130, -160, 18, 25, 78, -123, -132, 52, 29, 
+25, 34, -68, 23, 29, -71, 186, 47, -119, 65, 
+-97, 78, 115, -82, 96, 41, 82, 189, -24, -75, 
+-88, -81, -7, 34, 27, 82, 1, 145, -26, 272, 
+62, -85, -107, 48, -22, 71, 47, -7, -84, -151, 
+-44, 85, 21, -124, 17, 38, -88, 15, 5, -114, 
+35, 56, 108, 105, -137, -93, 51, 51, 51, 385, 
+57, 113, 95, 65, -31, 75, -77, -23, -48, 8, 
+231, -186, 68, -161, -47, 108, 6, -107, -71, 67, 
+-73, 21, 4, -65, 214, 63, -202, 18, -66, 85, 
+-79, -11, 50, 86, -120, -33, -47, -65, 176, 40, 
+-126, 91, 212, 103, -151, -48, 126, -70, 44, 77, 
+-92, -5, -324, -102, -25, -124, 163, -143, -44, 13, 
+144, -143, 116, 1, -98, 46, 19, -60, 6, -38, 
+11, 66, 158, -123, 213, -195, -15, 58, 28, -62, 
+-20, -49, -58, 84, 35, -69, 89, 30, 81, 62, 
+-82, -56, 74, 61, -2, 11, 127, -59, 54, -20, 
+-21, 109, 82, 81, 130, 2, 68, -31, 32, -13, 
+9, 59, -81, 209, -100, -121, 115, 79, 62, 62, 
+-1, -89, 7, -67, 97, -14, -82, -32, 41, -56, 
+-82, 24, 24, -50, -47, 23, -144, -140, -71, -21, 
+31, 147, 85, -15, -1, -100, -1, -28, 32, -82, 
+51, 153, -10, 40, 69, -40, 22, 1, 9, -77, 
+2, 49, 145, 95, 215, -76, 87, 18, 218, -80, 
+-83, -59, -212, -52, -75, 15, 34, 187, 95, -57, 
+-89, 49, -132, 183, 117, -46, -171, 135, -11, 123, 
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
+0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
+0, 0, };
+
+static int32_t expectedOutput[257] = {41.793, 29.547, 62.804, 13.301, 30.244, 92.505, 73.004, 16.088, 39.165, 28.565, 
+397.818, 107.527, 227.324, 326.219, 252.630, 236.845, 348.788, 346.156, 307.596, 81.489, 
+119.231, 386.307, 781.322, 650.677, 4.472, 932.892, 119.390, 775.034, 160.104, 1381.281, 
+105.035, 431.338, 968.341, 403.091, 2260.444, 2602.124, 172.271, 756.482, 729.475, 2469.544, 
+2243.920, 417.316, 1097.637, 6870.347, 2304.486, 1589.195, 2319.688, 847.306, 883.634, 3500.461, 
+948.353, 180.531, 494.383, 1056.905, 3569.024, 982.259, 2391.659, 161.476, 878.781, 2822.093, 
+5565.344, 8436.151, 394.526, 1561.058, 19329.914, 2010.601, 4256.051, 4926.558, 304.681, 13448.634, 
+2485.606, 11832.770, 1378.445, 3264.627, 1718.334, 1764.559, 1049.182, 14464.899, 1473.367, 817.662, 
+8396.957, 520.658, 2239.889, 849.025, 15582.139, 19490.272, 23440.474, 1644.372, 1913.855, 19156.438, 
+29745.160, 5876.234, 2762.751, 10619.644, 552.129, 24774.438, 1310.197, 21900.961, 1674.933, 5613.461, 
+8089.484, 409.633, 17441.347, 7008.733, 11234.895, 656.733, 3542.350, 3007.550, 9928.260, 16854.987, 
+6053.438, 16560.280, 3294.450, 23856.849, 19678.983, 11203.448, 0.615, 7918.839, 2774.248, 10782.992, 
+18452.319, 11057.048, 1274.963, 8468.807, 1130.146, 4669.219, 4388.148, 3958.819, 254.462, 312.217, 
+14514.237, 8802.849, 44763.958, 15104.682, 1220.948, 3079.284, 9531.956, 4051.949, 5650.696, 30471.124, 
+3961.616, 5315.119, 35552.214, 16916.787, 31987.554, 7729.508, 279.667, 2585.061, 14863.286, 1908.158, 
+17284.380, 5406.848, 58302.912, 8696.597, 12261.064, 12620.354, 25249.900, 1473.245, 70175.808, 19541.233, 
+25919.010, 3461.238, 9083.831, 30594.502, 29649.937, 202.284, 13854.657, 8469.216, 40957.897, 22713.893, 
+35164.292, 58826.560, 33064.688, 25885.807, 35401.682, 42017.109, 21948.014, 60640.447, 25599.418, 8466.198, 
+7381.225, 8595.859, 50810.863, 5644.499, 80997.817, 42252.142, 22622.722, 39179.824, 21446.828, 13150.507, 
+20916.670, 13420.185, 14764.970, 885.254, 6805.845, 73013.102, 124000.210, 13638.364, 17650.726, 14471.099, 
+10915.778, 17633.264, 39071.292, 13349.937, 18293.646, 10747.280, 27553.379, 48679.571, 6165.208, 36.014, 
+2457.017, 7071.023, 59298.388, 84843.107, 36258.245, 29973.386, 3600.866, 6922.307, 19285.446, 32902.281, 
+40621.090, 73906.670, 109.445, 72927.182, 11425.336, 85333.789, 56009.264, 9254.815, 12928.319, 2241.109, 
+19164.507, 48171.533, 55107.167, 23129.786, 44115.753, 1341.106, 27747.855, 23840.321, 9819.859, 22409.505, 
+31655.171, 10537.328, 75577.584, 680.272, 12754.621, 22293.294, 18971.696, 15261.967, 30979.096, 14007.789, 
+51674.210, 1581.374, 2554.650, 13922.540, 47176.238, 39530.189, 2660.395, };
+
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/test_mfcc/generateData.py b/Applications/KeywordSpotting/MXChip-SRNN/test/test_mfcc/generateData.py
new file mode 100644
index 000000000..384ce3d61
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/test_mfcc/generateData.py
@@ -0,0 +1,80 @@
+import numpy as np
+import python_speech_features as sp
+
+
+def hexToQ31(hexStr):
+    '''
+    hexStr in format 0x
+    '''
+    h = hexStr[2:]
+    value = bin(int(h, 16))
+    value = value[2:]
+    value = value.zfill(32)
+    valuef = ' '.join(value[i:i + 4] for i in range(0, 32, 4))
+    return value, valuef
+
+
+def q31ToF32(value):
+    num = 0.0
+    num += float(value[0])
+    for i, x in enumerate(value[1:]):
+        num += float(x) * (2 ** (-(i + 1)))
+    return num
+
+def createDataFile(inputData, expectedOutput, inputLen=512, outputLen=32):
+    assert len(inputData) == inputLen, len(inputData)
+    assert len(expectedOutput) == outputLen, len(expectedOutput)
+    inputStr = ''
+    for i, x in enumerate(inputData):
+        inputStr += '%.0f, ' % x
+        if (i+1) % 10 == 0:
+            inputStr += '\n'
+    outputStr = ''
+    for i, x in enumerate(expectedOutput):
+        outputStr += '%.3f, ' % x
+        if (i + 1) % 10 == 0:
+            outputStr += '\n'
+
+    template = '''
+static int32_t inputData[%d] = {%s};
+
+static int32_t expectedOutput[%d] = {%s};
+''' % (inputLen, inputStr, outputLen, outputStr)
+    return template
+
+def main():
+    np.random.seed(42)
+    # inputLen assumed to be same as frame len
+    inputLen = 512
+    frameLen = 400
+    preemph = 0.97
+    x = np.random.normal(size=frameLen)
+    x = x * 100
+    x = x.astype(int)
+    pre = np.concatenate([x[:1], x[1:] - preemph * x[:-1]])
+    pre_ = np.zeros(inputLen)
+    pre_[:frameLen] = pre[:]
+    pre = pre_
+    frames = sp.sigproc.framesig(pre, inputLen, inputLen)
+    assert len(frames) == 1
+    print("FFT")
+    print(np.fft.fft(frames[0], n=512)[:10])
+    powSpec = sp.sigproc.powspec(frames, inputLen)
+    powSpec = np.reshape(powSpec, -1)
+    print("powSpec")
+    # t = list(powSpec[-10:])
+    # t.reverse()
+    print(powSpec[:10])
+    fb, energy = sp.fbank(x, winlen=512/16000.0, nfilt=32, preemph=preemph)
+    print("FBank")
+    log = np.log(fb)
+    print(log)
+    x_inp = np.zeros(inputLen)
+    x_inp[:frameLen] = x[:]
+    template = createDataFile(x_inp, powSpec, inputLen=inputLen,
+                              outputLen=inputLen/2 + 1)
+    fp = open('data.h', 'w+')
+    print(template, file=fp)
+    fp.close()
+
+main()
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/test_mfcc/test_mfcc.cpp b/Applications/KeywordSpotting/MXChip-SRNN/test/test_mfcc/test_mfcc.cpp
new file mode 100644
index 000000000..8e3ffcf96
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/test_mfcc/test_mfcc.cpp
@@ -0,0 +1,90 @@
+#include <Arduino.h>
+#include "logfbank.h"
+#include "data.h"
+
+#define PRECISION 4
+
+static float32_t mfccResult[NFILT];
+static float32_t fbank_f32[NFILT * (NFFT/ 2 + 1)];
+#ifdef FFT_Q31
+    static q31_t fbank_q31[NFILT * (NFFT/ 2 + 1)];
+#endif
+
+#ifdef DEBUG_MODE
+void printVoid(void *val){
+    int32_t a = *((int32_t*)val);
+    Serial.println(a);
+}
+
+void printInt32(int32_t val){
+   Serial.println(val);
+}
+
+void printHexQ31(q31_t val){
+    char buff[20];
+    sprintf(buff, "%p", *(int*)&val);
+   Serial.println(buff);
+}
+
+void printFloatArrF32(float32_t *arr, int len, float scale){
+    for(int i = 0; i < len; i++){
+        float val = arr[i];
+        Serial.print((float)val * scale, PRECISION); Serial.print(", ");
+    }
+    Serial.println();
+    delay(1000);
+}
+
+void printFloatArrQ31(q31_t *arr, int len, float scale){
+    for(int i = 0; i < len; i++){
+        float32_t val;
+        arm_q31_to_float(&arr[i], &val, 1);
+        Serial.print((float)val * scale, PRECISION); Serial.print(", ");
+    }
+    Serial.println();
+    delay(1000);
+}
+#endif // DEBUG_MODE
+
+void setup(){
+    Serial.begin(115200);
+    delay(500);
+    Serial.println("Ready");
+    delay(500);
+    get_filterbank_parameters(fbank_f32, NFILT, SAMPLING_RATE, NFFT);
+    #ifdef FFT_Q31
+        arm_float_to_q31(fbank_f32, fbank_q31, NFILT * (NFFT/2 + 1));
+    #endif
+    delay(500);
+}
+
+
+void loop(){
+    #ifdef FFT_F32
+    void *_fbank = (void *) fbank_f32;
+    #elif FFT_Q31
+    void *_fbank = (void *) fbank_q31;
+    #endif
+    Serial.println("New Loop");
+    delay(1000);
+    unsigned long startTime = micros();
+    for(int i = 0; i < 100; i++){
+        logfbank(mfccResult, inputData, _fbank, (int32_t)0);
+    }
+    unsigned long endTime = micros();
+    float totalTime = (endTime - startTime) / 1000.0;
+    Serial.print("Time (ms) for 100 512 point MFCC is: ");
+    Serial.println(totalTime, 2);
+    #ifdef DEBUG_MODE
+    printFloatArrF32(mfccResult, NFILT, 1);
+    #endif
+    delay(1000);
+    Serial.println();
+}
+
+int main(){
+    setup();
+    for(int i = 0; i < 100; i++){
+        loop();
+    }
+}
\ No newline at end of file
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/test_mxchip_mic/readFromSerial.py b/Applications/KeywordSpotting/MXChip-SRNN/test/test_mxchip_mic/readFromSerial.py
new file mode 100644
index 000000000..c342eb948
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/test_mxchip_mic/readFromSerial.py
@@ -0,0 +1,34 @@
+from serial import Serial
+import scipy.io.wavfile as wav
+import numpy as np
+ 
+port = "COM3"
+baud = 115200
+samplingrate = 16000
+
+ser = Serial(port, baud, timeout=1)
+if ser.isOpen():
+     print(ser.name + ' is open...')
+
+lineList = []
+i=0
+while True:
+    print("\r Lines read: %5d" % len(lineList), end='')
+    lin = ser.readline()
+    lin = lin.decode("utf-8")
+    lin = lin.strip()
+    if len(lin) == 0:
+        continue
+    if "Done" in lin:
+        break
+    lineList.append(lin)
+
+ser.close()
+print()
+
+# Skip the first two and last two lines for good measure
+longStr = ''.join(lineList[2:-2])
+longStr = longStr.replace(' ', '')
+numList = np.array(longStr.split(',')[:-1]).astype(float)
+print("Writing %d (%fs) values" % (len(numList), len(numList)/samplingrate))
+wav.write('output.wav', samplingrate, numList)
\ No newline at end of file
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/test_mxchip_mic/test_mxchip_mic.cpp b/Applications/KeywordSpotting/MXChip-SRNN/test/test_mxchip_mic/test_mxchip_mic.cpp
new file mode 100644
index 000000000..de77939a4
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/test_mxchip_mic/test_mxchip_mic.cpp
@@ -0,0 +1,107 @@
+/*
+ * Step 1: Record data for 2 seconds
+ * Step 2: Send data through serial
+ * Step 3: Play data in python and see if its okay
+ * 
+  // Only 16bit is supported by the audio library. Comment here[1]
+  // The underlying BSP audio calls to mbed-os used here[2], defined here[3]
+  // and documented here[4] seems to support 16000Hz @16 bits
+  // [1] https://github.com/Microsoft/devkit-sdk/blob/d8f9c2cf1a26aa44b8aae13e3ef7247f3f730711/AZ3166/src/libraries/AudioV2/src/AudioClassV2.cpp#47
+  // [2] https://github.com/Microsoft/devkit-sdk/blob/d8f9c2cf1a26aa44b8aae13e3ef7247f3f730711/AZ3166/src/libraries/AudioV2/src/AudioClassV2.cpp#L101
+  // [3] https://github.com/Microsoft/devkit-sdk/blob/d8f9c2cf1a26aa44b8aae13e3ef7247f3f730711/AZ3166/src/libraries/AudioV2/src/stm32412g_discovery_audio.h#L295
+  // [4] https://os.mbed.com/users/the_sz/code/BSP_DISCO_F746NG_patch_fixed/docs/a4e658110084/group__STM32746G__DISCOVERY__AUDIO__Out__Private__Functions.html#ga18576073e3e3aca86934fc98288bc83a 
+ */
+
+#include <arm_math.h>
+#include <Arduino.h>
+#include <OledDisplay.h>
+#include <RingBuffer.h>
+#include <AudioClassV2.h>
+#include <circularq.h>
+
+// Must be a multiple of 512 hence 16384 instead of 16000.
+// The 512 requirement is because of the block size used
+// to push to the priority queue. Partial pushes fail.
+// Roughly 1 second audio
+#define AUDIO_SIZE (16384 * 3)
+// 2 times as we are using 16bit (2 char vs 1 char)
+#define AUDIO_BUFFER_SIZE (2 * AUDIO_SIZE)
+
+static AudioClass& Audio = AudioClass::getInstance();
+static char audio_container[AUDIO_BUFFER_SIZE];
+static FIFOCircularQ audioQ;
+char readBuffer[AUDIO_CHUNK_SIZE];
+
+
+void recordCallback(void) {
+  int length = Audio.readFromRecordBuffer(readBuffer, AUDIO_CHUNK_SIZE);
+  q_enqueue_batch(&audioQ, (void *)readBuffer, sizeof(char), length);
+}
+
+void printIdleMessage(){
+  Screen.clean();
+  Screen.print(0, "Audio Test");
+  Screen.print(1, "Hold A to Record", true);
+}
+
+void record(){
+  Serial.println("Start recording");
+  // Sampling rate 16000Hz @ 16 bit resolution 
+  Audio.format(16000U, 16U);
+  Audio.startRecord(recordCallback);
+}
+
+void setup(void){
+  pinMode(LED_BUILTIN, OUTPUT);
+  Serial.begin(115200);
+  Screen.init();
+  Serial.println("Testing 16bit@16000Hz audio");
+  q_init(&audioQ, (void *)audio_container, AUDIO_BUFFER_SIZE,
+        cb_write_char, cb_read_char);
+  // Initialize the button pin as a input
+  pinMode(USER_BUTTON_A, INPUT);
+  printIdleMessage();
+  delay(500);
+}
+
+void loop(void){
+  printIdleMessage();
+  while(digitalRead(USER_BUTTON_A));
+    
+  Screen.clean();
+  Screen.print(0, "Start recording:");
+  record();
+  while(!(q_is_full(&audioQ))){
+    delay(30);
+  }
+  if(Audio.getAudioState() == AUDIO_STATE_RECORDING){
+    Audio.stop();
+  }
+  Screen.print(0, "Recording done.");
+  Screen.print(1, "Printing to Serial.", true);
+  for(int i = 0; i < AUDIO_BUFFER_SIZE; i=i+2){
+    // Note that the audio returned is fake sterio, that is
+    // the same channel is repeated and interleaved to create
+    // a sterio effect. Hence we skip every other sample.
+    int16_t t = *(int16_t *)q_atN(&audioQ, i);
+    if (i % 4 == 0)
+      Serial.printf("%d, ", t);
+    if(i % 40 == 0){
+      Serial.println();
+      delay(10);
+    }
+  }
+  delay(100);
+  Serial.println("Done");
+  Screen.clean();
+  q_reset(&audioQ);
+  delay(100);
+}
+
+int main(){
+  setup();
+  Serial.println("Starting");
+  for(int i = 0; i < 100; i++)
+    loop();
+    delay(500);
+}
\ No newline at end of file
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/test_sfastrnn/sfastrnntest.py b/Applications/KeywordSpotting/MXChip-SRNN/test/test_sfastrnn/sfastrnntest.py
new file mode 100644
index 000000000..f930f92a1
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/test_sfastrnn/sfastrnntest.py
@@ -0,0 +1,131 @@
+import numpy as np
+
+def sigmoid(x):
+    return 1.0 / (1 + np.exp(-x).astype(np.float32))
+
+class FastRNN0:
+    def __init__(self):
+        self.timesteps = 4
+        self.featLen = 6
+        self.statesLen = 4
+        self.alpha = 0.2
+        self.beta = 0.8
+        W = [
+            0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0,
+            0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1,
+            0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2,
+            0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3,
+        ]
+        B = [1, 2, 3, 4,]
+        W = np.array(W)
+        self.W = np.reshape(W, [self.statesLen, self.statesLen + self.featLen])
+        self.B = np.array(B)
+
+    def cell(self, x, h):
+        W = self.W
+        B = self.B
+        hx = np.concatenate([h, x])
+        hcomb = np.matmul(W, hx)
+        h_ = hcomb  + B
+        h_ = sigmoid(h_)
+        h_ = self.alpha * h_ + self.beta * h
+        return h_
+
+    def unroll(self, x_list):
+        h = np.zeros(self.statesLen)
+        for x in x_list:
+            h = self.cell(x, h)
+        return h
+
+class FastRNN1:
+    def __init__(self):
+        self.timesteps = 3
+        self.featLen = 4
+        self.statesLen = 4
+        self.alpha = 0.3
+        self.beta = 0.7
+        W = [
+            0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0,
+            0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1,
+            0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8,
+            0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9,
+        ]
+        B = [0.1, 0.2, 0.3, 0.4,]
+        W = np.array(W)
+        self.W = np.reshape(W, [self.statesLen, self.statesLen + self.featLen])
+        self.B = np.array(B)
+
+    def cell(self, x, h):
+        W = self.W
+        B = self.B
+        hx = np.concatenate([h, x])
+        hcomb = np.matmul(W, hx)
+        h_ = hcomb  + B
+        h_ = sigmoid(h_)
+        h_ = self.alpha * h_ + self.beta * h
+        return h_
+
+    def unroll(self, x_list):
+        h = np.zeros(self.statesLen)
+        for x in x_list:
+            h = self.cell(x, h)
+        return h
+
+
+
+def main():
+    fastrnn0 = FastRNN0()
+    fastrnn1 = FastRNN1()
+    h0 = np.zeros(fastrnn0.statesLen)
+    inp1 = []
+    xx0 = np.array([
+        0.0,0.01,0.02,0.03,0.04,0.05,   0.06,0.07,0.08,0.09,0.1,0.11,
+        0.12,0.13,0.14,0.15,0.16,0.17,  0.18,0.19,0.2,0.21,0.22,0.23,
+    ])
+    xx1 = np.array([
+        0.12,0.13,0.14,0.15,0.16,0.17,  0.18,0.19,0.2,0.21,0.22,0.23,
+        0.24,0.25,0.26,0.27,0.28,0.29,  0.3,0.31,0.32,0.33,0.34,0.35,
+    ])
+    xx2 = np.array([
+        0.24,0.25,0.26,0.27,0.28,0.29,  0.3,0.31,0.32,0.33,0.34,0.35,
+        0.36,0.37,0.38,0.39,0.4,0.41,   0.42,0.43,0.44,0.45,0.46,0.47,
+    ])
+    xx3 = np.array([
+        0.36,0.37,0.38,0.39,0.4,0.41,   0.42,0.43,0.44,0.45,0.46,0.47,
+        0.0,0.01,0.02,0.03,0.04,0.05,   0.06,0.07,0.08,0.09,0.1,0.11,
+    ])
+    xx4 = np.array([
+        0.0,0.01,0.02,0.03,0.04,0.05,   0.06,0.07,0.08,0.09,0.1,0.11,
+        0.24,0.25,0.26,0.27,0.28,0.29,  0.3,0.31,0.32,0.33,0.34,0.35,
+    ])
+    xx5 = np.array([
+        0.12,0.13,0.14,0.15,0.16,0.17,  0.18,0.19,0.2,0.21,0.22,0.23,
+        0.36,0.37,0.38,0.39,0.4,0.41,   0.42,0.43,0.44,0.45,0.46,0.47,
+    ])
+    h0List = []
+    def predict(xx):
+        xx = np.reshape(xx, [-1, fastrnn0.featLen])
+        h0 = fastrnn0.unroll(xx)
+        if len(h0List) > 2:
+            del h0List[0]
+        h0List.append(h0)
+        if len(h0List) <= 2:
+            return h0, None
+        inp1 = np.array(h0List)
+        h1 = fastrnn1.unroll(inp1)
+        return h0, h1
+    h0, h1 = predict(xx0)
+    h0, h1 = predict(xx1)
+    h0, h1 = predict(xx2)
+    print(h1)
+    h0, h1 = predict(xx3)
+    print(h1)
+    h0, h1 = predict(xx4)
+    print(h1)
+    h0, h1 = predict(xx5)
+    print(h1)
+
+
+main()
+
+
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/test_sfastrnn/test_sfastrnn.cpp b/Applications/KeywordSpotting/MXChip-SRNN/test/test_sfastrnn/test_sfastrnn.cpp
new file mode 100644
index 000000000..d8b04cf82
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/test_sfastrnn/test_sfastrnn.cpp
@@ -0,0 +1,152 @@
+#include <Arduino.h>
+#include <OledDisplay.h>
+#include "sfastrnn.h"
+
+#define PRECISION 4
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+struct FastRNNParams fastrnnParams_test0;
+struct FastRNNParams fastrnnParams_test1;
+void initFastRNN_test0();
+void initFastRNN_test1();
+#ifdef __cplusplus
+}
+#endif
+static float combinedWMatrix0[] = {
+    0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0,
+    0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1,
+    0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2,
+    0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3,
+ };
+
+static float combinedBMatrix0[] = {1, 2, 3, 4};
+
+static float combinedWMatrix1[] = {
+    0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 
+    0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 
+    0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 
+    0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 
+ };
+
+static float combinedBMatrix1[] = {0.1, 0.2, 0.3, 0.4};
+
+void initFastRNN_test0() {
+    fastrnnParams_test0.timeSteps = 4;
+    fastrnnParams_test0.featLen = 6;
+    fastrnnParams_test0.statesLen = 4;
+    fastrnnParams_test0.W = combinedWMatrix0;
+    fastrnnParams_test0.b = combinedBMatrix0;
+    fastrnnParams_test0.alpha = 0.2;
+    fastrnnParams_test0.beta = 0.8;
+}
+
+void initFastRNN_test1() {
+    fastrnnParams_test1.timeSteps = 3;
+    fastrnnParams_test1.featLen = 4;
+    fastrnnParams_test1.statesLen = 4;
+    fastrnnParams_test1.W = combinedWMatrix1;
+    fastrnnParams_test1.b = combinedBMatrix1;
+    fastrnnParams_test1.alpha = 0.3;
+    fastrnnParams_test1.beta = 0.7;
+}
+
+unsigned verifyOutput(float *result, float *expected, unsigned length){
+    unsigned errorCount = 0;
+    for (int i = 0; i < length; i++){
+        if(abs(result[i] - expected[i]) > 0.00001)
+            errorCount += 1;
+    }
+    return errorCount;
+}
+
+unsigned testFastRNN(){
+    initFastRNN_test0();
+    initFastRNN_test1();
+    unsigned errorCode = 0;
+    struct SFastRNNParams2 sparams;
+    unsigned statesLen0 = fastrnnParams_test0.statesLen;
+    unsigned timeSteps1 = fastrnnParams_test1.timeSteps;
+    unsigned statesLen1 = fastrnnParams_test1.statesLen;
+    float h0container[statesLen0 * timeSteps1];
+    memset(h0container, 0, (statesLen0 * timeSteps1) * sizeof(float));
+    /*for(int i = 0; i < statesLen0 * timeSteps1; i++)*/
+        /*printf("%d %1.1f\n", i, h0container[i]);*/
+    /*printf(">>1\n\n");*/
+    initSFastRNN2(&sparams, &fastrnnParams_test0, &fastrnnParams_test1,
+            h0container);
+
+    float result_h[statesLen1];
+    float xx0[] = {
+        0.0,0.01,0.02,0.03,0.04,0.05,   0.06,0.07,0.08,0.09,0.1,0.11,
+        0.12,0.13,0.14,0.15,0.16,0.17,  0.18,0.19,0.2,0.21,0.22,0.23,
+    };
+    float xx1[] = {
+        0.12,0.13,0.14,0.15,0.16,0.17,  0.18,0.19,0.2,0.21,0.22,0.23,
+        0.24,0.25,0.26,0.27,0.28,0.29,  0.3,0.31,0.32,0.33,0.34,0.35,
+    };
+    float xx2[] = {
+        0.24,0.25,0.26,0.27,0.28,0.29,  0.3,0.31,0.32,0.33,0.34,0.35,
+        0.36,0.37,0.38,0.39,0.4,0.41,   0.42,0.43,0.44,0.45,0.46,0.47,
+    };
+    float xx3[] = {
+        0.36,0.37,0.38,0.39,0.4,0.41,   0.42,0.43,0.44,0.45,0.46,0.47,
+        0.0,0.01,0.02,0.03,0.04,0.05,   0.06,0.07,0.08,0.09,0.1,0.11,
+    };
+    float xx4[] = {
+        0.0,0.01,0.02,0.03,0.04,0.05,   0.06,0.07,0.08,0.09,0.1,0.11,
+        0.24,0.25,0.26,0.27,0.28,0.29,  0.3,0.31,0.32,0.33,0.34,0.35,
+    };
+    float xx5[] = {
+        0.12,0.13,0.14,0.15,0.16,0.17,  0.18,0.19,0.2,0.21,0.22,0.23,
+        0.36,0.37,0.38,0.39,0.4,0.41,   0.42,0.43,0.44,0.45,0.46,0.47,
+    };
+    SFastRNNInference2(&sparams, xx0, result_h);
+    SFastRNNInference2(&sparams, xx1, result_h);
+    SFastRNNInference2(&sparams, xx2, result_h);
+    float exp_h0[] ={0.60863139, 0.62427422, 0.58376938, 0.60744043};
+    if(verifyOutput(result_h, exp_h0, statesLen1)) {
+        errorCode |= 1;
+    }
+    SFastRNNInference2(&sparams, xx3, result_h);
+    float exp_h1[] ={0.60897788, 0.62464093, 0.58391194, 0.60766335};
+    if(verifyOutput(result_h, exp_h1, statesLen1)) {
+        errorCode |= 2;
+    }
+    SFastRNNInference2(&sparams, xx4, result_h);
+    float exp_h2[] ={0.60866079, 0.62443474, 0.58348852, 0.60734866};
+    if(verifyOutput(result_h, exp_h2, statesLen1)) {
+        errorCode |= 4;
+    }
+    SFastRNNInference2(&sparams, xx5, result_h);
+    float exp_h3[] ={0.60841268, 0.62415074, 0.5834325, 0.60720676};
+    if(verifyOutput(result_h, exp_h3, statesLen1)) {
+        errorCode |= 8;
+    }
+    return errorCode;
+}
+
+void setup(){
+    Screen.init();
+    Serial.begin(115200);
+    delay(500);
+    Serial.println("Ready");
+    delay(500);
+}
+
+int main(){
+    setup();
+    char buffer[30];
+    for(int i = 0; i < 100; i++){
+        Serial.print("New Loop - ");
+        unsigned errorCode = testFastRNN();
+        Serial.printf("Error Code: %d\n", errorCode);
+        Screen.print(1, "Hello my dude", false);
+        sprintf(buffer, "Error code %d", errorCode);
+        Screen.print(2, buffer, false);
+        delay(1000);
+        Screen.clean();
+        delay(500);
+    }
+}
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/time_lstm/generateLSTMParams.py b/Applications/KeywordSpotting/MXChip-SRNN/test/time_lstm/generateLSTMParams.py
new file mode 100644
index 000000000..18ee63cbd
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/time_lstm/generateLSTMParams.py
@@ -0,0 +1,53 @@
+import numpy as np
+from lstmtemplate import getFile
+
+def sigmoid(x):
+    return 1.0 / (1 + np.exp(-x).astype(np.float32))
+
+class LSTM:
+    def __init__(self, kernel, bias, forgetBias = 1.0):
+        assert bias.ndim == 1
+        hidDim = int(len(bias) / 4)
+        assert kernel.ndim == 2
+        assert kernel.shape[0] == 4 * hidDim
+        assert kernel.shape[1] - hidDim > 0
+        self.kernel = kernel
+        self.bias = bias
+        self.forgetBias = forgetBias
+        self.hidDim = hidDim
+        self.featLen = self.kernel.shape[1] - self.hidDim
+
+    def cell(self, x, h, c):
+        '''
+        Non batched version for simplicity
+        '''
+        assert x.ndim == 1
+        assert x.shape[0] == self.featLen
+        h_ = h.copy()
+        x_ = np.concatenate([x, h_], axis=0)
+        combOut = np.matmul(self.kernel, x_)
+        combOut = combOut + self.bias
+        i, j, f, o = np.split(combOut, 4, axis=0)
+        new_c = c * sigmoid(f + self.forgetBias) + sigmoid(i) * np.tanh(j)
+        new_h = np.tanh(new_c) * sigmoid(o)
+        new_o = sigmoid(o)
+        c = new_c
+        h = new_h
+        o = new_o
+        return h, c
+
+
+def main():
+    inputDim = 32
+    hiddenDim = 32
+    # [hid + feat, 4 * hid]
+    kernel =  np.random.normal(size=(4 * hiddenDim, hiddenDim + inputDim))
+    bias = np.random.normal(size=hiddenDim * 4)
+    x =  np.random.normal(size=inputDim)
+    h0, c0 = np.zeros(hiddenDim), np.zeros(hiddenDim)
+
+    lstm = LSTM(kernel, bias)
+    h_final, c = lstm.cell(x, h0, c0)
+    print(getFile(inputDim, kernel, bias, x, h_final))
+
+main()
\ No newline at end of file
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/time_lstm/lstmtemplate.py b/Applications/KeywordSpotting/MXChip-SRNN/test/time_lstm/lstmtemplate.py
new file mode 100644
index 000000000..f60611c0f
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/time_lstm/lstmtemplate.py
@@ -0,0 +1,102 @@
+def getFile(inputDim, kernel, bias, x, h_final):
+	hiddenDim = int(kernel.shape[0] / 4)
+	inputDim_ = int(kernel.shape[1] - hiddenDim)
+	assert inputDim_ == inputDim
+	kernelStr = ''
+	for i in range(kernel.shape[0]):
+		for j in range(kernel.shape[1]):
+			val = kernel[i][j]
+			kernelStr += '%2.5f, ' % val
+		kernelStr += '\n'
+	
+	biasStr = ''
+	for i in range(bias.shape[0]):
+		val = bias[i]
+		biasStr  += '%2.5f, ' % val
+
+	xStr = ''
+	for i in range(x.shape[0]):
+		val  = x[i]
+		xStr += '%2.5f, ' % val
+	timeSteps = 10
+	template = 	'''
+/*
+ * h_final = %r
+ */
+#include <OledDisplay.h>
+#include "lstm.h"
+#include <Arduino.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+struct LSTMParams lstmParams_test;
+void initLSTM_test();
+#ifdef __cplusplus
+}
+#endif
+static float combinedWMatrix[] = {
+	%s
+};
+
+static float combinedBMatrix[] = {
+	%s
+};
+
+void initLSTM_test() {
+    lstmParams_test.timeSteps = %d;
+    lstmParams_test.featLen = %d;
+    lstmParams_test.statesLen = %d;
+    lstmParams_test.forgetBias = 1.0;
+    // W is [4 * statesLen, (featLen + staetsLen)] flattened (i.e. row major)
+    // (first row, second row, .. .. 4 * statesLen-th row)
+    lstmParams_test.W = combinedWMatrix;
+    // 4 * statesLen
+    lstmParams_test.B = combinedBMatrix;
+}
+
+void setup(){
+    Screen.init();
+    Serial.begin(115200);
+}
+
+void loop(){
+    // print a string to the screen with wrapped = false
+    Screen.print("Hello my dude", false);
+    delay(1000);
+	initLSTM_test();
+
+	float x[] = {%s};
+	float result_c_h_o[3 * lstmParams_test.statesLen];
+	for(int i = 0; i < 3 * lstmParams_test.statesLen; i++){
+		result_c_h_o[i] = 0;
+	}
+
+	LSTMStep(&lstmParams_test, x, result_c_h_o, result_c_h_o);
+    for (int i = 0; i <  lstmParams_test.statesLen; i++) {
+        int j = i + lstmParams_test.statesLen;
+        float val = result_c_h_o[j];
+    }
+	unsigned long StartTime = millis();
+	for (int i = 0; i < 100; i++)
+		LSTMStep(&lstmParams_test, x, result_c_h_o, result_c_h_o);
+	unsigned long CurrentTime = millis();
+	unsigned long ElapsedTime = CurrentTime - StartTime;
+	Serial.print("Time taken for 100 runs (ms): ");
+	Serial.println(ElapsedTime);
+    Serial.print("Inp :"); Serial.print(lstmParams_test.featLen);
+    Serial.print(" Hid: "); Serial.println(lstmParams_test.statesLen);
+
+    delay(1000);
+    // Clean up the screen
+    Screen.clean();
+    delay(1000);
+}
+
+int main(){
+	setup();
+	for(int i = 0; i < 100; i++)
+		loop();
+}
+''' % (h_final, kernelStr, biasStr, timeSteps, inputDim, hiddenDim, xStr)
+	return template
\ No newline at end of file
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/time_lstm/test_time_lstm.cpp b/Applications/KeywordSpotting/MXChip-SRNN/test/time_lstm/test_time_lstm.cpp
new file mode 100644
index 000000000..7361b15ce
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/time_lstm/test_time_lstm.cpp
@@ -0,0 +1,215 @@
+
+/*
+ * h_final = array([-6.71087606e-01, -1.03132299e-03,  6.56800629e-05,  4.51847417e-02,
+       -8.02446761e-02,  3.53527338e-01,  2.11524042e-06, -7.61333996e-01,
+        3.18349005e-03,  1.28556791e-05, -3.36101384e-01, -1.00514994e-03,
+       -3.19386475e-02,  7.30757544e-01,  5.99826879e-01, -1.10188531e-02,
+       -9.76548390e-03,  6.93357436e-01,  1.31418979e-02,  6.34115905e-05,
+        2.84080052e-01, -3.20119432e-01,  2.09135008e-05,  2.55955767e-08,
+        7.07285625e-04, -6.45787700e-10, -6.89922962e-04, -1.20176965e-07,
+        3.20921020e-05,  1.34223647e-07,  8.53080020e-03, -3.69366082e-01])
+ */
+#include <OledDisplay.h>
+#include "lstm.h"
+#include <Arduino.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+struct LSTMParams lstmParams_test;
+void initLSTM_test();
+#ifdef __cplusplus
+}
+#endif
+static float combinedWMatrix[] = {
+	-0.58519, -0.67889, -0.58548, 0.66239, 1.66877, -2.04633, -0.90499, -0.44286, 1.55263, -0.12704, 1.94729, 0.22296, 1.75172, 1.22881, -1.00343, -0.95171, 0.57279, -0.97251, 1.37922, -0.73468, 0.98781, -0.16056, -0.43780, 0.76965, -0.22528, 0.54672, -0.25075, 0.81634, -1.26560, 2.17531, -0.41969, 1.07547, -0.33286, 0.40364, 0.49471, 1.33241, -0.11244, 1.54385, 2.19004, 0.88032, -1.39531, 0.41660, 1.32557, 1.64492, 0.47534, -1.06931, 0.61036, -0.65341, 0.34520, 1.24856, -2.23269, 1.23635, -0.46464, -0.58830, -0.22311, 0.50413, -0.48230, 0.40561, 0.13406, 0.96818, 0.85829, 0.30877, 0.11098, 0.25127, 
+0.21568, 0.89326, -0.58914, 0.51631, -0.46550, -0.64453, 1.88184, -2.51278, 0.45288, 0.68703, 0.33804, -0.09902, 0.90298, 0.24077, 0.62384, 0.52447, -0.08076, -0.46334, 0.01476, 1.25743, 2.28601, 0.59501, -0.96664, -0.43238, 0.33130, -0.24327, -0.39466, 0.56855, -1.02932, -0.78985, 1.06101, 0.31716, -0.20102, -0.98703, 0.91727, 0.35986, 0.51243, -0.37342, -0.40922, -0.52155, 0.98470, 0.29673, -0.61873, 1.76273, -1.09229, -2.27965, 0.29485, -0.73824, 0.09882, 1.02719, 1.75132, -0.85647, 0.04490, 0.83979, -1.40102, 1.19157, 0.25300, -0.35287, -0.91364, 0.99137, 0.85060, 0.85204, 0.43696, 1.55777, 
+0.53652, -0.40073, -0.89541, -1.09954, 0.02670, 0.63416, -0.25361, -0.95270, -0.07732, 1.31793, 0.83414, -0.05604, 0.04828, -0.03534, 2.57229, -2.12015, 0.55550, 1.33460, 0.57300, -1.33056, 1.10481, -0.15975, -0.77737, -0.47626, 1.25486, -0.91504, 1.70741, -1.57712, 0.74065, 1.88588, -0.57965, 0.70678, -0.30761, 0.26736, -0.39388, 1.26963, 1.43449, 1.26984, -1.25620, 0.60849, -0.22611, -0.34662, -0.29789, 1.32117, -1.81797, 0.07593, -0.71847, -0.41354, -0.43430, 2.45024, 1.09733, 0.90802, 1.04410, -0.70144, -0.61707, -0.47829, -0.82087, 0.07048, -0.57282, -0.53880, 0.40690, 0.79260, -0.54353, 0.58542, 
+-0.52580, -1.52541, 1.33277, 0.93528, 0.16442, 0.17705, -0.37466, -0.70837, -1.61864, 0.55278, 0.83687, -0.31205, -2.63356, -2.10208, -1.55385, 1.46260, 0.40727, -0.68623, -1.23413, 1.46821, -1.24583, -0.05938, 1.26395, 0.41626, 0.06038, -1.01588, -0.15965, -0.93485, -0.19117, 1.01529, 0.43241, -1.31771, -1.74707, -0.82872, 0.71618, 0.94310, 0.31655, -0.96958, 0.61213, 0.12114, 2.17410, -0.05207, 1.52862, -0.06259, 1.32984, 0.64033, 0.80217, 0.75300, 0.71802, 0.91793, 2.13634, 0.51589, -1.54719, 0.66606, 0.04771, 0.46342, -0.55845, 0.38593, -0.32902, 0.27681, 1.36006, 0.76892, -1.47886, 0.79048, 
+0.06658, -0.33758, 0.42711, -0.75914, -0.19327, 1.07704, 0.40293, 0.40475, 0.29175, 0.05775, -0.65674, 0.00218, -0.49494, 0.80846, 0.88702, 1.80265, 2.13552, 1.31512, 0.31141, 0.79081, -1.08289, -0.73842, -0.75600, 0.26759, -1.91233, -0.35756, -0.46881, -0.34417, 0.14607, 0.33016, 1.37993, 1.66362, -0.49982, 0.10979, 0.80312, 0.04736, 0.19150, 2.12592, -2.92529, 2.21138, 0.31735, 1.59702, 2.46597, -0.42708, 0.61659, -1.01800, 0.26045, 2.31326, -0.70891, -1.39273, 0.88756, -0.11900, 0.35809, -0.54200, 0.43856, -0.18809, -0.98399, -1.14180, -1.12045, -2.04883, 0.10117, 1.19537, 1.23180, -1.59500, 
+1.51036, 0.03522, 0.75122, 2.16603, -0.47328, 1.72191, 0.90994, 0.58949, -0.14161, 0.39743, 0.45654, -2.58791, -0.06440, -1.04100, -1.37581, 0.60896, -2.23679, -0.20917, 0.65131, -0.64964, 0.58916, 1.27981, 0.45992, 0.21029, -2.33814, -0.84473, -1.37716, 0.97680, 0.05402, -0.96222, 0.06681, 1.84997, -1.53229, -1.90832, -2.44418, -1.76298, -0.33935, -0.62765, -1.35407, -0.91377, -0.23197, -0.33236, 1.24495, -2.63022, -1.40868, 0.38768, -0.60313, -0.50425, 1.54533, -0.01888, -0.40638, -0.35348, -0.06811, -0.04744, -0.73023, -0.88560, 1.11848, -2.02043, 0.94268, 0.57315, 0.32464, -0.12049, -0.53646, 1.27914, 
+1.33265, 0.20846, -0.36428, -0.56692, -1.12421, -0.74946, 0.57123, -1.35930, 1.58954, 1.57803, -0.40513, -0.94201, 0.86166, 1.02887, 3.27554, -0.13901, -0.94149, -2.12636, -0.06617, -1.21817, 0.54693, -1.64074, -0.82593, -1.82546, 1.84122, -0.65460, 0.74693, 0.04440, -1.68976, 0.93912, 0.54835, -0.74640, -0.45999, -0.66707, -1.91899, 0.91409, 2.29627, -0.66342, 0.07487, -0.18522, -0.85505, -0.44945, -1.06738, -0.12120, 1.16921, -1.65578, 1.35577, -2.37817, 0.06942, -1.89768, -0.38112, -0.14324, 0.45083, -0.22330, -2.14272, 1.04331, 0.15306, -1.05129, -0.18770, -1.28618, 0.21478, 1.04099, 0.41818, 0.63579, 
+-0.40000, 1.08378, 1.21116, -0.24618, 0.85914, 0.73398, -0.09944, -2.13482, 0.54285, -1.02514, 0.58060, -0.67567, -0.59939, -1.94512, -0.35465, -0.32791, 0.06993, -1.50554, 0.70149, -0.48328, 0.69180, 1.44660, 1.19775, -1.14540, -0.04751, 1.53017, -1.01144, 1.20073, -0.30881, 0.48837, -0.07383, 0.63331, 0.25391, 0.92241, 1.66889, 0.36349, 0.72516, 0.43282, 0.35302, -0.07570, 1.73000, -1.52690, 1.03855, -1.58233, -0.14203, -0.17524, 0.05972, -1.48941, -0.67373, 0.49048, 0.25553, -0.61720, 0.05553, 0.34320, 1.42696, -2.54999, -0.15923, 0.54998, 0.50282, 0.35703, -1.04039, 0.41253, -0.41267, -0.01784, 
+0.87524, -0.38165, 1.96810, -0.55404, -0.26169, -2.75142, 0.37636, 0.63236, -1.31422, -0.07175, -0.98249, -0.53589, -0.53918, -0.06593, -0.59131, -0.49761, -0.43501, -1.03297, -0.99587, 1.80756, 0.27702, -0.50010, 1.37540, -0.31474, -1.10167, 0.88362, 0.87287, -1.76269, -0.34452, 0.85744, -0.49203, 0.96459, 1.61304, 0.17574, 0.95295, 1.33515, 0.71527, 0.42316, -1.53681, 1.32175, -0.06307, -0.87212, -1.78005, -0.11774, -0.42793, 0.38406, -0.27971, 1.41042, -1.12085, -0.45681, 0.79879, 0.55974, 1.31207, -0.36507, -1.37264, -1.15540, 0.59876, -1.44234, -0.83990, 1.54946, 0.29719, 0.25104, 1.94130, 1.43717, 
+1.48063, 0.85512, 1.89647, 0.76804, -0.63754, -0.62482, -1.71864, -2.19824, -1.42768, 1.39065, -1.29676, -0.43946, -1.65871, -0.54534, 0.20029, -0.96311, 0.72771, -0.16363, -1.16303, 0.15941, 1.04193, -1.58877, -0.80974, 0.29560, -1.02179, -1.34949, -0.05867, 1.33166, -0.00593, 0.02216, -0.70059, 1.35114, -0.18670, -0.17418, 1.60021, -0.45857, 0.32786, -1.51128, 0.77037, 0.11008, -0.86712, 1.83075, -0.12849, 1.21747, 2.95851, 0.43697, 1.30739, 0.63331, -0.19143, 0.26125, -0.39663, 0.16476, 0.54746, -0.98632, -1.31770, 0.27863, 1.03685, -1.14529, -0.25870, 0.80852, -1.81847, 1.23807, -1.10234, -0.27737, 
+-0.95219, 0.59926, 0.96942, -0.00254, -0.13620, 0.27283, 0.40559, -1.68304, -0.90610, -1.07282, -0.29295, 0.19426, 1.01568, 1.65626, -1.02188, 0.51059, 1.94026, -0.21767, -0.64401, 0.65873, 0.50681, 0.60919, 1.15367, -1.29279, -0.64584, 0.40139, -0.14502, 0.18681, 0.48618, -0.38070, -0.51295, -0.97966, -0.17806, -0.04619, -0.67646, -0.48164, 0.61722, 1.24432, -0.92367, 0.17432, 0.35519, -0.24444, -1.19349, -0.75541, -1.29536, 0.65132, -0.11658, 1.32318, 1.61128, 0.23328, -0.69564, 0.16007, 0.34405, 0.97051, -0.78793, -0.03871, -0.55617, 0.01502, 0.43729, 1.09949, -0.66846, -0.07247, -0.38547, -0.65714, 
+-1.35967, -0.21746, 0.34835, -1.42747, 1.08207, 0.24840, -0.72708, -1.76349, 0.83272, 2.18695, 0.73015, 0.18298, 0.12276, -0.81261, -1.26827, 0.46284, 1.90358, -0.89704, 1.30032, -0.68099, -0.32194, 0.12370, 0.75693, -1.28796, 0.90296, 0.77608, -0.08263, -0.90727, 0.34395, -1.40068, 1.33076, 1.75074, 0.85803, 0.40375, 1.35216, -1.80753, -0.30478, 0.24584, -0.66830, -1.54420, 0.90713, -0.45398, 0.49492, 1.31106, 0.64361, -1.31564, 0.65985, 0.16889, -0.22491, 1.58818, -0.67005, 0.82641, -2.58285, -0.35277, -0.58256, 0.46022, 0.06887, 2.13783, -0.92914, 0.76317, -0.29014, -1.90849, -0.28795, 0.96194, 
+-0.61529, 0.56741, 0.17557, -0.05848, 0.30029, -1.62098, -0.05451, 0.74009, -0.67645, -0.19926, 2.19765, 0.06368, 1.17916, -2.00938, 0.61314, 0.86513, -0.85155, 1.75109, 0.53425, 0.34792, -0.79942, 1.50103, -0.80813, 0.41402, -1.06662, 0.67697, -0.06088, -1.04746, -0.00966, -0.04847, -0.27658, -1.28783, 0.01362, -0.34272, 0.34237, -1.14109, 1.58304, -0.28441, -1.08521, 1.08766, -1.70535, 0.21995, -0.71381, -0.48679, 0.18029, 1.44083, 0.54971, -1.44737, -0.20511, 0.51439, -0.26631, -0.92850, -1.11819, 0.29438, 0.52410, 0.97130, 0.40439, 1.03826, 0.71844, -1.21432, 0.30951, 0.86678, -0.18459, -1.03964, 
+-0.20591, 0.00808, -1.11923, -0.94402, -0.12144, 0.58467, 0.01654, 0.10058, 1.54402, -0.72495, 0.51887, -1.25087, 1.19518, 2.95573, -0.87809, -0.21256, 0.07885, 1.34868, -0.92554, -0.76654, 0.42873, 0.75793, 0.50064, 1.50193, 1.82276, 0.08824, -0.56143, -1.13649, -1.05642, 0.87808, 0.36278, 0.61532, 1.04187, 1.14801, 1.34006, -0.18048, -1.08772, 0.76403, 1.26523, 0.29162, -0.06674, 0.60644, 2.50410, 1.99756, 0.27915, 1.76207, -0.61717, 0.04345, 0.25934, 0.90624, 1.86057, 0.31798, -0.94505, -2.36416, -1.15872, -0.60045, -1.04507, -0.43484, 2.37246, -1.88466, -1.66850, -0.34509, 0.47339, 1.58891, 
+0.73775, 0.17748, -0.58397, 0.09369, -1.29801, -0.32233, 2.22078, -0.29508, -0.93911, 0.03328, -0.08617, -0.10239, -0.15745, 1.01933, -0.67637, 1.21999, 0.06967, -0.87091, 0.72857, -0.86144, 0.23016, -0.39724, 0.75668, 1.20682, 1.87019, -0.74277, 0.30517, 1.14098, -0.08807, -0.68411, 0.36372, 3.00492, 1.10993, -0.69445, -1.77052, -0.92357, 0.73522, 0.84834, -0.88666, -0.07124, -0.34409, -0.85464, 0.07321, -0.93645, -1.70467, -0.82758, 0.25127, 0.69022, -0.78322, -1.04244, -0.45877, 0.11258, -0.44608, -0.13900, 0.38455, -1.07706, 0.44236, 0.86087, 1.18115, -0.22989, 0.06153, -0.58584, 0.74635, -0.28866, 
+-1.00202, -0.05342, 1.97870, 0.28951, 1.11957, -0.82622, -1.53332, 0.14747, -0.56023, -0.86144, 0.58648, -0.37112, -1.25066, 0.19753, -1.26705, -0.86452, 0.54894, 1.32704, -1.60699, -1.24158, -0.24475, -1.05420, 0.45878, -1.50104, -1.50431, 1.29019, 1.01545, -0.50879, -0.14243, 0.33607, 0.39060, 0.01881, 0.85215, 0.69526, 1.36657, -0.70863, 1.57899, -1.10042, 1.52195, -1.62922, 0.26183, 0.50191, -0.93809, -0.06372, 0.09904, -1.00476, 0.31125, -1.40900, 0.12506, 0.33649, 0.38747, 1.42744, 0.51897, 0.86344, -1.18413, 3.05899, -1.03968, 1.60335, -1.40853, -1.55791, 1.05269, -0.12011, 0.78423, 0.91494, 
+0.00653, -1.12771, -1.73828, -0.51973, -0.98796, 1.23725, 0.48968, 0.33129, 0.31347, 0.60463, -1.05366, -0.68933, -1.44466, -0.65293, 0.47101, 2.25338, 0.65792, -2.76953, 0.92772, -0.55352, 0.73934, 0.67864, 0.17244, 1.33542, -2.24167, -0.78141, -0.66128, 0.58246, -0.11231, 0.44568, -1.95760, -1.33394, 1.31892, 0.05451, -0.17498, 0.22606, 0.05626, 0.78177, 1.29171, 0.75959, -0.11648, -1.23648, 0.15112, 1.62479, -0.17637, 1.08069, -0.43780, -0.74071, -0.38356, -0.10069, -2.06468, -0.06981, 0.04176, -0.78981, 1.02433, -0.38658, -1.55362, 0.13897, -0.06073, 0.85195, -1.11290, 0.79297, 0.51009, 1.06127, 
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+-2.43378, 1.16718, -0.29049, -0.13432, -1.01133, 1.54728, 0.61775, -0.89955, 0.19892, -1.04356, -1.76862, -1.31121, 1.16595, 0.69027, -0.99196, -1.81120, 0.53965, -0.41247, -0.14112, 1.43961, -0.99613, 0.52360, -0.16746, 0.78256, -0.30163, -0.91609, -0.48290, -2.72319, 1.10098, 0.95324, -0.62835, -0.26596, -1.06363, 0.64828, -0.89394, 1.67112, -0.95337, 1.37428, 0.09942, -0.04849, -0.05791, -1.38120, -0.25628, -0.81427, 0.43317, 1.48203, -1.58460, -0.60732, -1.33930, 1.39845, 1.95511, 0.21363, 0.71282, 0.13847, -0.78627, -0.15139, -0.46751, -0.42285, 0.57471, -1.42024, 0.02021, -0.26016, 0.75675, 0.59361, 
+1.31134, -0.13098, -2.00734, -0.93406, 1.58324, 0.50003, 0.13689, -0.87196, 0.05833, 0.99665, 0.98327, 0.98988, 0.89339, 1.03809, 0.78621, 0.88103, 1.53572, -1.39729, 1.27808, -0.26566, -0.06674, 0.34556, -1.13794, 2.29858, -0.17696, -0.43553, 0.15821, -0.30896, -0.49620, -0.62003, 1.00067, 0.03897, 0.86280, -2.42888, -0.63050, -0.26623, -0.30546, 1.44125, -0.26496, -0.45090, -2.59505, -1.30308, 0.03894, 1.04728, 0.48377, 0.48296, 0.60754, 0.42086, -0.26480, -0.88336, -0.65156, -0.51292, -1.00359, -1.41580, -0.59354, 0.36956, -0.10194, -0.08152, -0.04885, -1.66350, 1.98368, -0.11607, -0.44747, -1.05446, 
+0.31919, -0.61309, 1.60072, 0.45702, 1.68852, 1.41356, -0.62903, -1.33228, 0.73355, 0.60525, -0.76326, -1.31492, -0.91790, 0.77836, 1.21115, -0.44691, 0.35436, -1.13773, 0.22315, -0.58719, -1.93163, -1.86830, -1.34928, -0.34800, -0.82163, 0.39710, 0.24323, -0.49472, 1.52686, -0.72595, -0.58865, 0.96621, 1.47062, -0.45425, -0.23638, -0.27010, 0.83252, -0.28955, 1.27675, 0.35390, 2.15685, 0.23572, 0.61856, -1.81310, -0.66225, -1.01558, 0.07135, 0.87471, -0.93605, -0.43320, 0.28074, -0.71422, 0.90045, -0.26706, 0.45304, -0.96900, -1.25666, 0.35489, -1.08641, -1.38098, 0.54523, 0.18682, 0.24350, -0.60203, 
+-1.28576, 0.08662, 0.75453, -0.85266, 0.16132, 1.23335, 1.14932, -0.37122, -0.47374, -0.65105, 0.20082, 0.75391, 1.51517, -1.46028, -0.77918, -0.14623, 0.03314, -0.50141, 1.48913, -0.37523, 0.95608, -0.54086, -0.20026, 1.29441, -0.77102, 1.09111, -0.17800, -2.14594, 1.30067, -0.26293, 1.07502, -1.14430, 1.04201, -0.30873, 0.08366, 0.66268, -0.24409, 0.97253, 0.44211, 0.73492, -0.30636, 2.27052, 1.23347, 0.43512, -0.30998, -0.48104, 1.32982, 0.16214, 0.17947, -0.65147, -0.60378, 0.69977, -1.02732, -0.19270, 0.28238, 0.50688, -0.35395, -0.08357, 0.74348, 0.07401, 1.18937, -0.51207, -0.68522, -0.45468, 
+-0.25655, -1.81461, -1.01455, 1.28570, 0.23582, -0.91208, -1.20081, 1.06752, -0.65302, -0.66323, -1.94758, -1.42050, 0.01954, -1.58466, -0.50957, -0.59811, 1.00651, 0.29189, -0.09513, -2.08497, -0.07785, 0.31931, -1.00295, 1.21390, 0.42442, 0.16516, 2.03173, -0.50764, -1.41511, -0.70048, 0.72566, -0.12342, -1.67429, -1.48115, -0.45534, -0.95356, 1.30400, 0.85432, -0.27132, -1.05450, -1.10344, 0.55857, 1.65066, -1.11683, 1.03619, 0.48329, 0.18891, -0.32099, 0.70457, 0.38589, 0.42398, -0.29999, -0.21103, -0.84580, 0.71547, 1.00467, 0.95272, 1.03182, 2.11385, 0.48845, 0.11580, 0.40509, -0.98081, 1.88289, 
+-1.00195, -2.47029, -0.39231, 1.51470, -1.30410, 0.26493, 0.87864, 0.02813, 0.09031, 0.84352, -0.54666, 2.21042, 1.39825, 0.70020, 0.46239, 2.78841, 0.13084, -0.34402, 0.06773, 1.28960, 1.29416, 0.65501, -0.33913, -0.94201, -0.42162, -0.56685, -0.89190, -1.98801, -0.69122, 0.99499, -0.98489, 1.43923, -2.06378, -0.04518, -0.27583, -1.29387, -1.15648, -0.81079, -0.90490, 0.79017, 1.47878, -0.33207, -1.07583, 1.09302, 1.21923, 0.18068, -0.66074, 0.49256, 0.28278, 2.20322, 1.63843, 0.53992, 0.02191, -1.36204, -0.91066, -0.22597, -0.48994, -1.14178, 0.09680, -0.12962, 0.61681, 0.45765, -1.02377, -0.34936, 
+0.48278, -0.10909, -1.86943, -0.37650, -1.38507, -1.61859, 0.61621, 0.64238, -0.83736, 0.21661, -1.95292, -1.20899, -0.56437, -1.00792, 0.53964, -0.31451, -0.16589, -0.31872, -0.10152, 0.16592, -1.70339, 2.32733, -0.38647, 0.88063, -1.65341, 0.02848, -0.59739, -1.81835, 0.67206, -2.24083, -0.87310, -0.10186, 1.52665, -0.46748, 0.05970, -0.66257, -0.36698, 0.94774, 0.12699, -0.46268, 0.53417, 0.39448, 1.58317, -0.60305, -0.29536, 0.25510, 0.06026, 0.31369, -0.49323, -1.24672, -0.56113, -0.88201, -1.10129, 1.20542, 0.13435, 0.62233, 1.31860, 0.43947, -0.23949, 0.02335, 1.94285, -1.06811, -0.27367, -1.32945, 
+-0.19557, 0.03289, 0.23699, 0.02193, 0.77532, 0.06321, 1.03667, 0.89826, 0.24111, 1.67922, 0.04711, -1.33425, 0.19713, -0.00201, 0.20935, 0.91439, -1.40974, -0.30333, 1.69111, 2.16792, 1.35641, 1.11563, -0.73544, 0.01227, -0.14725, -0.06232, -0.24038, -0.29440, -0.04491, 0.34455, -0.88821, -0.41155, 0.04259, 0.54885, -1.77159, 0.69347, 0.34039, 0.00708, 1.09252, -0.00243, 0.11856, 0.86008, 0.52716, -0.10066, 0.25756, -0.91698, -0.68245, 0.29978, -1.24074, 0.21934, 0.84363, -1.71938, -0.93181, -0.49826, 1.07815, 0.25475, -0.24071, 1.75808, -0.64675, -0.31299, -0.13181, 0.79136, -0.43765, -0.60329, 
+1.49109, -0.26684, -1.21487, -0.27912, 1.53077, 0.39661, -1.07599, 1.11025, 0.54383, 1.88582, -1.05229, 0.38689, -1.09319, 0.69637, 1.15841, 1.37729, 1.59648, -0.35578, 0.22528, 0.95012, -0.54138, 0.96081, -1.43280, -0.45092, 0.42463, -0.21574, 0.26036, -0.53047, -1.81373, 0.08625, 0.43978, 1.25338, 0.69635, -0.62035, 0.75300, 0.16638, 0.96760, 0.89251, 0.93753, 0.07012, -0.18235, -0.75170, -0.69281, 0.68020, -0.77610, 0.36808, 0.74171, -0.22074, -0.45656, 1.23506, 0.53328, 0.12383, 0.30385, -1.82138, 0.64436, -0.06789, -2.12688, -1.16072, 1.30753, 0.68161, 0.70475, 2.06023, 3.38877, 0.63612, 
+
+};
+
+static float combinedBMatrix[] = {
+	0.63996, -0.09585, -0.15097, -1.47944, -0.47541, -0.56039, -2.40899, 0.10089, 1.33360, 0.90108, -2.08068, 0.59518, 1.20934, 1.51987, -0.43165, -0.61395, 0.01621, 1.81715, 0.07818, -1.25586, -0.85403, -0.91166, -0.16168, 1.26379, -0.22939, 1.12440, -0.08515, -1.54148, 0.45774, -0.18015, 1.09907, 1.87869, 0.70034, 1.70314, -0.18178, -0.34356, -0.02675, 0.18986, -0.42086, -0.31883, 1.64913, 0.81516, -0.26547, -1.41190, 1.64127, 2.15529, 2.32116, -1.50958, 1.34902, -0.20767, -0.11413, -0.38524, -0.68853, 0.84848, 0.26761, 1.35052, -0.27536, 1.34197, -1.76423, -0.25488, -1.78216, 0.67825, -0.66977, -0.39316, -1.97726, -0.44405, 0.27705, -0.00839, 0.68054, -0.62942, 1.22864, -0.35865, 0.85756, 0.84449, -0.30799, -0.29482, 0.62865, -0.10990, 0.67245, -0.42014, -0.27578, -0.86982, -2.34731, 0.85418, 0.13671, 1.27893, -1.05396, -0.11259, -0.31550, -1.68557, 0.58558, -0.71730, 0.59818, -1.87533, 0.76829, 0.84665, -1.00321, 0.09345, -0.56404, 0.37090, -0.55149, 0.25336, -1.86605, -0.45549, 0.58586, 0.10328, -0.68767, 0.74014, 0.45099, 0.65472, 0.35438, 0.22475, -0.38266, 0.15669, -0.92177, 0.62432, -0.11862, -1.11691, -1.27925, -1.12514, 1.55283, -0.41950, -0.60443, -0.20458, -1.01081, -1.41413, 0.61988, -0.03117, 
+};
+
+void initLSTM_test() {
+    lstmParams_test.timeSteps = 10;
+    lstmParams_test.featLen = 32;
+    lstmParams_test.statesLen = 32;
+    lstmParams_test.forgetBias = 1.0;
+    // W is [4 * statesLen, (featLen + staetsLen)] flattened (i.e. row major)
+    // (first row, second row, .. .. 4 * statesLen-th row)
+    lstmParams_test.W = combinedWMatrix;
+    // 4 * statesLen
+    lstmParams_test.B = combinedBMatrix;
+}
+
+void setup(){
+    Screen.init();
+    Serial.begin(115200);
+}
+
+void loop(){
+    // print a string to the screen with wrapped = false
+    Screen.print("Hello my dude", false);
+    delay(1000);
+	initLSTM_test();
+
+	float x[] = {-0.77274, 0.83537, 0.45031, -0.68209, 2.12232, -1.34356, 0.46644, 0.11524, 0.31190, 0.16921, 1.07484, 0.98511, -0.48373, -0.49206, -2.97712, 1.72739, 0.20630, 0.90967, 1.50899, -0.20455, 0.81700, 0.59895, 0.14991, -0.56331, 0.49994, -0.09532, -0.23706, 0.98108, -1.63718, 1.24303, 0.25937, 0.74287, };
+	float result_c_h_o[3 * lstmParams_test.statesLen];
+	for(int i = 0; i < 3 * lstmParams_test.statesLen; i++){
+		result_c_h_o[i] = 0;
+	}
+
+	LSTMStep(&lstmParams_test, x, result_c_h_o, result_c_h_o);
+    for (int i = 0; i <  lstmParams_test.statesLen; i++) {
+        int j = i + lstmParams_test.statesLen;
+        float val = result_c_h_o[j];
+    }
+	unsigned long StartTime = millis();
+	for (int i = 0; i < 100; i++)
+		LSTMStep(&lstmParams_test, x, result_c_h_o, result_c_h_o);
+	unsigned long CurrentTime = millis();
+	unsigned long ElapsedTime = CurrentTime - StartTime;
+	Serial.print("Time taken for 100 runs (ms): ");
+	Serial.println(ElapsedTime);
+    Serial.print("Inp :"); Serial.print(lstmParams_test.featLen);
+    Serial.print(" Hid: "); Serial.println(lstmParams_test.statesLen);
+
+    delay(1000);
+    // Clean up the screen
+    Screen.clean();
+    delay(1000);
+}
+
+int main(){
+	setup();
+	for(int i = 0; i < 100; i++)
+		loop();
+}
+
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn/generateSFastRNNParams.py b/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn/generateSFastRNNParams.py
new file mode 100644
index 000000000..96984ea4e
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn/generateSFastRNNParams.py
@@ -0,0 +1,98 @@
+import numpy as np
+from template import getTemplate as getTemplateC
+from template_MXChip import getTemplate as getTemplateMXChip
+
+def sigmoid(x):
+    return 1.0 / (1 + np.exp(-x).astype(np.float32))
+
+class FastRNN:
+    def __init__(self, W, B, alpha, beta, timeSteps, featLen, statesLen):
+        self.timeSteps = timeSteps
+        self.featLen = featLen
+        self.statesLen = statesLen
+        self.alpha = alpha
+        self.beta =  beta
+        self.W = W
+        self.B = B
+
+    def cell(self, x, h):
+        W = self.W
+        B = self.B
+        hx = np.concatenate([h, x])
+        hcomb = np.matmul(W, hx)
+        h_ = hcomb  + B
+        h_ = sigmoid(h_)
+        h_ = self.alpha * h_ + self.beta * h
+        return h_
+
+    def unroll(self, x_list):
+        h = np.zeros(self.statesLen)
+        for x in x_list:
+            h = self.cell(x, h)
+        return h
+
+
+def main(device='C'):
+    assert device in ['C', 'MXChip']
+    inputDim = 32
+    hiddenDim0 = 32
+    timeSteps0 = 8
+    hiddenDim1 = 16
+    timeSteps1 = 8
+    alpha0, beta0 = 0.2, 0.8
+    alpha1, beta1 = 0.1, 0.9
+    W0 = np.random.normal(size=[hiddenDim0, hiddenDim0 +  inputDim])
+    B0 = np.random.normal(size=hiddenDim0)
+    W1 = np.random.normal(size=[hiddenDim1, hiddenDim1 + hiddenDim0])
+    B1 = np.random.normal(size=hiddenDim1)
+    x0 = np.random.normal(size=[timeSteps0, inputDim])
+    x1 = np.random.normal(size=[timeSteps0, inputDim])
+    fastrnn0 = FastRNN(W0, B0, alpha0, beta0, timeSteps0, inputDim, hiddenDim0)
+    fastrnn1 = FastRNN(W1, B1, alpha1, beta1, timeSteps1, hiddenDim0, hiddenDim1)
+
+    hList = []
+    for i in range(timeSteps1):
+        h = fastrnn0.unroll(x0)
+        hList.append(h)
+    inp = np.array(hList)
+    assert inp.shape[0] == timeSteps1
+    assert inp.shape[1] == hiddenDim0
+    hout0 = fastrnn1.unroll(inp)
+
+    h = fastrnn0.unroll(x1)
+    hList.append(h)
+    inp = np.array(hList[-timeSteps1:])
+    assert inp.shape[0] == timeSteps1
+    assert inp.shape[1] == hiddenDim0
+    hout1 = fastrnn1.unroll(inp)
+
+    h = fastrnn0.unroll(x1)
+    hList.append(h)
+    inp = np.array(hList[-timeSteps1:])
+    assert inp.shape[0] == timeSteps1
+    assert inp.shape[1] == hiddenDim0
+    hout2 = fastrnn1.unroll(inp)
+
+    expected = ''
+    for tx in hout0:
+        expected += '%f, ' % tx
+    expected += '\\n'
+    for tx in hout1:
+        expected += '%f, ' % tx
+    expected += '\\n'
+    for tx in hout2:
+        expected += '%f, ' % tx
+    expected += '\\n'
+    if device == 'C':
+        ret = getTemplateC(W0, B0, alpha0, beta0, W1, B1, alpha1, beta1,
+                    timeSteps0, timeSteps1, x0, x1, expected)
+    elif device == 'MXChip':
+        ret = getTemplateMXChip(W0, B0, alpha0, beta0, W1, B1, alpha1, beta1,
+                    timeSteps0, timeSteps1, x0, x1, expected)
+    print(ret)
+
+
+
+
+device = 'MXChip'
+main(device)
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn/template.py b/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn/template.py
new file mode 100644
index 000000000..b12f4da5c
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn/template.py
@@ -0,0 +1,135 @@
+def getTemplate(W0, B0, alpha0, beta0, W1, B1, alpha1, beta1, timeSteps0,
+                timeSteps1, x0, x1, expected):
+    statesLen0 = len(B0)
+    featLen0 = W0.shape[1] - statesLen0
+    statesLen1 = len(B1)
+    featLen1 = W1.shape[1] - statesLen1
+    assert W0.shape[0] == statesLen0
+    assert W1.shape[0] == statesLen1
+    assert x0.shape[0] == timeSteps0
+    assert x0.shape[1] == featLen0
+    assert featLen1 == statesLen0
+
+    W0Str = ''
+    for i in range(W0.shape[0]):
+        for j in range(W0.shape[1]):
+            W0Str += '%f, ' % W0[i][j]
+        W0Str += '\n'
+
+    B0Str = ''
+    for i in range(B0.shape[0]):
+        B0Str += '%f,' % B0[i]
+
+    W1Str = ''
+    for i in range(W1.shape[0]):
+        for j in range(W1.shape[1]):
+            W1Str += '%f, ' % W1[i][j]
+        W1Str += '\n'
+
+    B1Str = ''
+    for i in range(B1.shape[0]):
+        B1Str += '%f,' % B1[i]
+
+    xx0Str = ''
+    for i in range(x0.shape[0]):
+        for j in range(x0.shape[1]):
+            xx0Str += '%f, ' % x0[i][j]
+        xx0Str += '\n'
+    xx1Str = ''
+    for i in range(x1.shape[0]):
+        for j in range(x1.shape[1]):
+            xx1Str += '%f, ' % x1[i][j]
+        xx1Str += '\n'
+
+    return    '''
+#include "algorithms/includes/sfastrnn.h"
+#include <stdio.h>
+
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+struct FastRNNParams fastrnnParams_test0;
+struct FastRNNParams fastrnnParams_test1;
+void initFastRNN_test0();
+void initFastRNN_test1();
+#ifdef __cplusplus
+}
+#endif
+static float combinedWMatrix0[] = {
+    %s
+ };
+
+static float combinedBMatrix0[] = {%s};
+
+static float combinedWMatrix1[] = {
+    %s
+ };
+
+static float combinedBMatrix1[] = {%s};
+
+void initFastRNN_test0() {
+    fastrnnParams_test0.timeSteps = %d;
+    fastrnnParams_test0.featLen = %d;
+    fastrnnParams_test0.statesLen = %d;
+    fastrnnParams_test0.W = combinedWMatrix0;
+    fastrnnParams_test0.b = combinedBMatrix0;
+    fastrnnParams_test0.alpha = %f;
+    fastrnnParams_test0.beta = %f;
+}
+
+void initFastRNN_test1() {
+    fastrnnParams_test1.timeSteps = %d;
+    fastrnnParams_test1.featLen = %d;
+    fastrnnParams_test1.statesLen = %d;
+    fastrnnParams_test1.W = combinedWMatrix1;
+    fastrnnParams_test1.b = combinedBMatrix1;
+    fastrnnParams_test1.alpha = %f;
+    fastrnnParams_test1.beta = %f;
+}
+
+unsigned testFastRNN(){
+    initFastRNN_test0();
+    initFastRNN_test1();
+    unsigned errorCode = 0;
+    struct SFastRNNParams2 sparams;
+    unsigned statesLen0 = fastrnnParams_test0.statesLen;
+    unsigned timeSteps1 = fastrnnParams_test1.timeSteps;
+    unsigned statesLen1 = fastrnnParams_test1.statesLen;
+    float h0container[statesLen0 * timeSteps1];
+    memset(h0container, 0, (statesLen0 * timeSteps1) * sizeof(float));
+    initSFastRNN2(&sparams, &fastrnnParams_test0, &fastrnnParams_test1,
+            h0container);
+
+    float result_h[statesLen1];
+    float xx0[] = {
+        %s
+    };
+    float xx1[] = {
+        %s
+    };
+    for(int i = 0; i < timeSteps1; i++)
+        SFastRNNInference2(&sparams, xx0, result_h);
+
+    SFastRNNInference2(&sparams, xx0, result_h);
+    for(int i =0; i < statesLen1; i++)
+        printf("%%2.5f ", result_h[i]);
+    printf("\\n");
+    SFastRNNInference2(&sparams, xx1, result_h);
+    for(int i =0; i < statesLen1; i++)
+        printf("%%2.5f ", result_h[i]);
+    printf("\\n");
+    SFastRNNInference2(&sparams, xx1, result_h);
+    for(int i =0; i < statesLen1; i++)
+        printf("%%2.5f ", result_h[i]);
+    printf("\\n");
+    printf("Expected\\n%s");
+    return errorCode;
+}
+
+int main(){
+    unsigned errorCode = testFastRNN();
+}
+''' % (W0Str, B0Str, W1Str, B1Str, timeSteps0, featLen0, statesLen0, alpha0,
+       beta0, timeSteps1, featLen1, statesLen1, alpha1, beta1, xx0Str, xx1Str,
+       expected)
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn/template_MXChip.py b/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn/template_MXChip.py
new file mode 100644
index 000000000..50f78bb10
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn/template_MXChip.py
@@ -0,0 +1,152 @@
+def getTemplate(W0, B0, alpha0, beta0, W1, B1, alpha1, beta1, timeSteps0,
+                timeSteps1, x0, x1, expected):
+    statesLen0 = len(B0)
+    featLen0 = W0.shape[1] - statesLen0
+    statesLen1 = len(B1)
+    featLen1 = W1.shape[1] - statesLen1
+    assert W0.shape[0] == statesLen0
+    assert W1.shape[0] == statesLen1
+    assert x0.shape[0] == timeSteps0
+    assert x0.shape[1] == featLen0
+    assert featLen1 == statesLen0
+
+    W0Str = ''
+    for i in range(W0.shape[0]):
+        for j in range(W0.shape[1]):
+            W0Str += '%f, ' % W0[i][j]
+        W0Str += '\n'
+
+    B0Str = ''
+    for i in range(B0.shape[0]):
+        B0Str += '%f,' % B0[i]
+
+    W1Str = ''
+    for i in range(W1.shape[0]):
+        for j in range(W1.shape[1]):
+            W1Str += '%f, ' % W1[i][j]
+        W1Str += '\n'
+
+    B1Str = ''
+    for i in range(B1.shape[0]):
+        B1Str += '%f,' % B1[i]
+
+    xx0Str = ''
+    for i in range(x0.shape[0]):
+        for j in range(x0.shape[1]):
+            xx0Str += '%f, ' % x0[i][j]
+        xx0Str += '\n'
+    xx1Str = ''
+    for i in range(x1.shape[0]):
+        for j in range(x1.shape[1]):
+            xx1Str += '%f, ' % x1[i][j]
+        xx1Str += '\n'
+
+    return    '''
+#include "sfastrnn.h"
+#include <OledDisplay.h>
+#include <Arduino.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+struct FastRNNParams fastrnnParams_test0;
+struct FastRNNParams fastrnnParams_test1;
+void initFastRNN_test0();
+void initFastRNN_test1();
+#ifdef __cplusplus
+}
+#endif
+static float combinedWMatrix0[] = {
+    %s
+ };
+
+static float combinedBMatrix0[] = {%s};
+
+static float combinedWMatrix1[] = {
+    %s
+ };
+static float combinedBMatrix1[] = {%s};
+
+void initFastRNN_test0() {
+    fastrnnParams_test0.timeSteps = %d;
+    fastrnnParams_test0.featLen = %d;
+    fastrnnParams_test0.statesLen = %d;
+    fastrnnParams_test0.W = combinedWMatrix0;
+    fastrnnParams_test0.b = combinedBMatrix0;
+    fastrnnParams_test0.alpha = %f;
+    fastrnnParams_test0.beta = %f;}
+
+void initFastRNN_test1() {
+    fastrnnParams_test1.timeSteps = %d;
+    fastrnnParams_test1.featLen = %d;
+    fastrnnParams_test1.statesLen = %d;
+    fastrnnParams_test1.W = combinedWMatrix1;
+    fastrnnParams_test1.b = combinedBMatrix1;
+    fastrnnParams_test1.alpha = %f;
+    fastrnnParams_test1.beta = %f;
+}
+
+unsigned testFastRNN(){
+    initFastRNN_test0();
+    initFastRNN_test1();
+    unsigned errorCode = 0;
+    struct SFastRNNParams2 sparams;
+    unsigned statesLen0 = fastrnnParams_test0.statesLen;
+    unsigned timeSteps1 = fastrnnParams_test1.timeSteps;
+    unsigned statesLen1 = fastrnnParams_test1.statesLen;
+    float h0container[statesLen0 * timeSteps1];
+    memset(h0container, 0, (statesLen0 * timeSteps1) * sizeof(float));
+    initSFastRNN2(&sparams, &fastrnnParams_test0, &fastrnnParams_test1,
+            h0container);
+
+    float result_h[statesLen1];
+    static float xx0[] = {
+        %s
+    };
+    static float xx1[] = {
+        %s
+    };
+    for(int i = 0; i < timeSteps1; i++)
+        SFastRNNInference2(&sparams, xx0, result_h);
+    unsigned long StartTime = millis();
+    for(int i = 0; i < 100; i++)
+        SFastRNNInference2(&sparams, xx1, result_h);
+    unsigned long CurrentTime = millis();
+    unsigned long ElapsedTime = CurrentTime - StartTime;
+    Serial.print("Time taken for 100 runs (ms): ");
+    Serial.println(ElapsedTime);
+    Serial.print("Layer 0 [");
+    Serial.print("Inp: "); Serial.print(fastrnnParams_test0.featLen);
+    Serial.print(" hDim: "); Serial.print(statesLen0);
+    Serial.print(" ts: "); Serial.print(fastrnnParams_test0.timeSteps);
+    Serial.println("]");
+    Serial.print("Layer 1 [");
+    Serial.print("Inp: "); Serial.print(fastrnnParams_test1.featLen);
+    Serial.print(" hDim: "); Serial.print(statesLen1);
+    Serial.print(" ts: "); Serial.print(fastrnnParams_test1.timeSteps);
+    Serial.println("]");
+    return errorCode;
+}
+
+void setup() {
+    Screen.init();
+    Serial.begin(115200);
+    Serial.println("Ready");
+}
+
+void loop() {
+    Serial.println("Loop");
+    testFastRNN();
+    Serial.println();
+    delay(1000);
+
+}
+
+int main(){
+    setup();
+    delay(500);
+    for(int i = 0; i < 100; i++)
+        loop();
+}
+''' % (W0Str, B0Str, W1Str, B1Str, timeSteps0, featLen0, statesLen0, alpha0,
+       beta0, timeSteps1, featLen1, statesLen1, alpha1, beta1, xx0Str, xx1Str)
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn/time_sfastrnn.cpp b/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn/time_sfastrnn.cpp
new file mode 100644
index 000000000..f52cd87a9
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn/time_sfastrnn.cpp
@@ -0,0 +1,169 @@
+
+#include "sfastrnn.h"
+#include <OledDisplay.h>
+#include <Arduino.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+struct FastRNNParams fastrnnParams_test0;
+struct FastRNNParams fastrnnParams_test1;
+void initFastRNN_test0();
+void initFastRNN_test1();
+#ifdef __cplusplus
+}
+#endif
+static float combinedWMatrix0[] = {
+    -1.463001, -1.608238, -0.515035, 0.212683, -0.490496, 0.081317, -0.126735, 2.546996, 0.426095, -1.516358, -0.517311, 1.043285, -1.461927, 0.033619, 0.167512, -0.612685, 1.287924, -0.908442, -0.999037, -0.022562, 0.865150, 0.515413, -0.293145, 2.513954, 0.815144, -0.550754, -0.503369, 1.094128, 0.875122, -0.788792, -0.177555, -0.835176, 0.497470, -1.288407, -2.259994, 1.334897, 1.268324, 1.025427, -0.633709, 0.062466, -0.543856, -0.879896, -1.765943, -2.458441, 0.724722, -0.948708, -0.628804, 1.798527, 0.959637, -0.486350, -0.556546, 0.162826, -1.222148, -0.254480, 0.011908, 1.344277, -0.840661, 0.671354, -0.542394, 1.876581, -0.658842, 1.402100, 0.075175, -1.919560, 
+-0.096117, 0.733164, 2.115817, -0.771978, -1.151200, -0.863722, -1.065490, 1.410405, 0.680804, 0.030813, 0.734885, -0.110043, 1.160063, 1.850488, 1.084261, 0.473697, 0.239441, 0.386820, 0.347727, -0.526016, -1.426372, 0.363248, 1.360431, 0.770462, 0.473252, 1.213022, 0.336275, 1.579441, 0.232998, -1.431587, -1.425107, 0.528384, 0.319200, 0.364835, 0.847596, -0.959305, 1.824354, 1.198022, 0.052803, -0.130849, 1.615890, -2.067542, 0.206611, 0.084601, -1.984883, -1.286008, -0.183897, -1.068569, 1.470677, -2.702302, -0.665119, -0.346067, 1.251984, 0.445639, -0.956230, 0.234531, -0.102565, 0.313286, -0.755711, -2.137626, -0.727585, 1.146454, -0.694576, -0.789521, 
+0.641563, 0.483000, -0.465223, 1.269212, 1.633147, 0.002212, -0.205266, -0.432667, 0.543417, -0.657155, -0.827909, -0.544024, -0.271605, 0.714078, 0.188222, 0.961605, -1.082005, 0.528004, 1.197721, 0.625790, -0.531735, 1.496538, -0.623211, -0.039617, 0.256738, -1.122342, 1.528594, -0.738589, 0.434754, -1.350794, -1.007540, -2.232016, 0.983118, -0.406300, 0.319870, -0.003049, -0.896247, 0.565264, 0.137492, -1.327265, -1.107797, 1.816275, -0.345457, 1.293157, 0.333309, -0.054301, 0.619463, -1.385102, -0.533470, 1.346377, 1.562904, -0.223482, -1.455293, 0.737545, -0.128256, 1.240807, -1.493310, 0.080344, -0.165168, 1.231474, -0.374610, 0.711336, -1.477542, -0.470964, 
+-0.656386, 1.576045, -0.335401, 0.427575, 0.702232, 0.723065, -0.426154, -0.353560, 0.672140, -1.271332, 0.783266, -1.209061, -0.416216, -0.327486, -1.827358, 0.764994, -1.425648, 0.231708, -0.914214, 1.673060, 0.724350, -0.310104, 1.158892, -1.961689, 0.281941, 0.583069, -1.409553, -0.145918, -0.261622, 1.761118, -0.061617, -1.341284, -1.332836, -0.208020, 0.925792, 0.135631, 0.174579, -1.209099, 0.957962, -1.745917, -0.792186, -0.248138, -0.165440, -0.148935, -1.307238, 0.563775, 0.284707, -1.169652, -2.828754, -1.185601, 1.131491, 0.656182, -1.055730, 0.818997, -0.085208, -0.905625, 1.798508, 0.722879, 0.509572, -0.323854, -0.321564, -0.252345, 0.381476, -0.368613, 
+-0.074919, -0.233678, -0.123059, 0.278319, 1.068024, -0.629322, -0.691175, -1.014975, -0.599052, 0.507058, 0.002995, 0.762335, 0.380925, -0.606949, 0.725976, 0.849606, -1.460235, -1.087340, -0.742382, -0.654407, 0.093633, -0.058831, -0.104262, 0.758625, 0.765124, 2.554762, 1.594256, -1.133789, -0.611893, -0.087930, 2.320282, 0.343533, 0.446773, 0.169784, -0.118240, 0.955247, -1.891241, 0.914840, -0.759616, -0.196301, -1.654649, -0.291182, 0.490403, 0.589910, 0.633847, -1.056975, -0.388852, 0.025353, 0.944651, 0.755763, -0.751081, -0.604684, -0.609690, -0.177046, 0.443386, 0.887866, -1.463714, -1.163942, -1.393820, -0.804748, 0.697293, -0.311335, -0.044319, -1.359249, 
+1.380995, -1.288235, -1.710079, -0.063513, -0.902670, 0.500920, -1.924562, -0.553001, 0.097762, -0.363627, 0.652919, 2.294359, -0.307750, 0.643806, 0.547697, 0.114237, 0.737595, -1.995646, -1.084810, 0.926421, -0.712689, 0.732767, -1.330412, -1.068075, -0.463778, -0.585043, 1.070094, 1.063924, -2.929486, 1.142738, 0.825487, 0.007441, 2.138249, -1.749018, 0.824951, 2.171068, -0.648621, -0.275690, 1.510950, -0.584865, -1.253189, -0.337552, 0.401746, 0.079840, -0.652001, -0.137461, -1.162070, -2.020258, 0.498568, 0.604669, 0.797579, 0.567278, -0.204495, 0.192825, 1.735941, -2.033776, 1.035516, 1.017701, -0.877548, 1.512696, 0.518580, 2.249872, -0.601333, 0.846658, 
+0.510734, 0.621803, 0.803452, 0.351683, 1.638468, -0.682519, -0.404083, 0.125560, -0.648148, 0.982958, 0.698376, -2.289114, 0.044634, 0.518226, -0.412493, -2.071861, -0.324956, -1.839477, 1.093226, 0.526581, -0.433123, -0.684160, -0.680004, -0.524009, 1.577912, -0.898682, -0.158502, -0.258205, 0.240414, 2.575841, 0.683980, -0.329290, 0.916843, 0.448556, 0.720534, 2.257772, 0.382073, 0.495522, -0.569758, 0.581957, -0.476345, -0.111119, 0.600198, 0.249427, 1.124021, 0.284441, -0.922101, -1.753768, -0.280319, 1.644985, 0.281135, -0.436874, 0.575186, 1.212380, -1.181544, 1.954728, 1.151511, -0.049220, 0.338226, 0.505781, 0.980844, 0.136317, 0.732253, -0.824807, 
+0.345609, -0.009857, 0.251267, -1.761950, 0.563583, 0.391451, -0.632284, -1.958941, -0.212659, 1.783886, -0.615789, 1.502364, 2.157413, -1.027152, 0.003323, -1.715427, -0.033989, -0.843667, -0.238556, 0.461586, 1.589210, -0.542165, 0.817914, -0.282953, 0.250836, 0.788768, -0.152013, 1.192482, -1.220499, -1.098319, 1.127349, -0.472195, 0.967432, 2.601111, -0.172669, 0.846395, -0.523399, 2.056826, -0.241081, -0.785224, -0.408702, -0.272545, 0.627815, -0.741877, -0.573766, 1.221598, 0.311954, 0.313567, 1.390818, 0.938386, -0.764971, 1.154589, 0.384211, 0.518375, 0.100117, 0.004455, -1.156828, 1.035057, -0.414093, 0.534238, 1.665964, -1.238557, 0.191084, -1.861732, 
+-1.035307, 0.959128, 0.743735, 0.789472, 1.246437, 0.701509, 0.891643, 0.837673, -0.722578, -1.595812, 1.774627, 1.330297, 0.496482, 0.112419, 0.075825, 0.295705, -0.843171, 0.490802, 0.581542, -0.249847, 0.350289, 0.640288, -0.320067, -0.331324, 0.823975, -1.368907, 0.467728, 0.074980, 0.698910, 0.902905, -1.121681, 0.504973, -1.145824, 0.599793, 0.017627, -0.552890, 1.391353, 0.205946, -1.119405, -0.095463, -1.174210, 0.609547, 0.731730, -0.938541, 1.273829, -0.480448, 0.866022, -0.310405, 0.343858, 0.309644, -1.695875, -0.113766, 0.526702, -0.704588, 0.381117, 0.555971, -0.083298, 0.223039, -0.603921, 0.554882, 1.513572, 0.606811, -0.697302, 1.084783, 
+-0.824411, 1.212255, 0.251716, 0.991289, 0.787050, 1.810049, -1.381291, -0.239204, 0.740711, -0.260828, -0.552805, -0.366561, 0.607902, 0.382302, -0.586720, 0.340685, -1.742842, -0.181700, 1.129976, 1.117269, 0.614889, 1.217334, 0.378709, 0.400297, 1.908038, 1.839118, -0.816531, -0.704213, 1.800338, 0.233622, -0.525752, -0.018464, 0.730329, -0.028267, 0.656068, 0.705036, -0.197044, 0.180971, -0.829405, 0.674120, 0.323508, 0.768608, 0.391449, 0.243296, 0.300709, -2.348033, -1.414001, -0.200398, 1.102434, -2.231932, 1.732161, -0.326020, 1.140528, 1.271278, 0.522694, -1.078432, 0.015626, -1.056439, -1.037853, -0.918159, 0.094147, 0.469082, -0.683776, -0.354973, 
+-0.357925, -0.141059, 0.025697, 0.630057, 0.933137, 1.421486, -0.291685, -0.140247, 0.987696, -1.827731, 0.127905, -1.067744, 0.713877, -0.313759, -0.236097, 1.118592, 0.651725, 0.333785, 0.652355, -0.664347, 0.780957, -1.628334, 1.238844, 0.014120, -0.632629, 0.021157, -0.521321, -0.838450, 1.468607, -0.189616, -1.577276, 2.086128, 2.033473, 0.656807, -1.157364, 0.368168, 0.413005, -0.117281, 0.620078, 0.611162, -0.140577, -1.295135, 1.304626, 1.044958, -1.356967, -0.996963, -1.348607, 2.027726, 0.475379, -1.892355, 0.645032, -0.803315, 0.434021, 1.027189, -0.043401, 1.278151, 1.254890, -0.111974, 0.180807, 0.621952, 0.604677, 1.077822, 0.084286, -0.074407, 
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+1.748960, -1.250720, 0.647673, 1.419473, 0.554151, 0.653606, 0.565125, 0.972337, 0.477990, -0.611764, 1.151175, -0.201181, 0.549160, 0.049755, 0.938319, -0.849869, 0.243877, -0.754113, 0.052802, 1.075256, -1.181837, -0.803333, -0.818565, 1.137928, 1.264775, -0.039846, -1.798116, -1.617380, -1.666136, -1.003204, 0.955179, 0.004328, -1.764123, 0.485474, 1.429926, 0.915478, -0.472447, 0.100746, -1.470929, -0.535812, 1.715648, 0.592510, 0.253959, -0.140632, -0.431525, -1.370412, 0.941578, -2.366421, 1.379079, -0.388469, 0.957313, 0.756529, 0.494682, -0.277110, 2.277600, -0.003246, -0.865847, 1.755084, 0.159255, -0.144554, -0.397216, -0.316015, -0.873992, -0.341031, 
+
+ };
+
+static float combinedBMatrix0[] = {-0.821923,-0.770436,-1.457529,-0.692116,0.917840,0.855654,0.098558,-0.968530,0.791547,0.461784,-0.812138,-0.573899,-0.658041,1.791518,0.109328,0.234418,0.200769,0.361127,-0.902484,0.074987,0.408439,-1.952910,0.673328,0.180265,-1.684048,-0.455957,-1.291929,0.483835,0.251530,0.240671,-0.566100,-1.027852,};
+
+static float combinedWMatrix1[] = {
+    -0.079572, 1.010639, -1.775012, 1.601770, 0.380464, 1.700940, 1.682144, 0.004111, 0.554301, 0.538089, 0.748991, 0.367411, 0.380426, 0.829259, 0.047680, 0.597817, 0.760077, 0.729829, 0.499416, 0.469848, -0.390372, 0.741787, 0.391093, -1.696635, -0.751937, 0.013258, -0.680191, -0.001031, 0.194471, -1.018807, 0.798910, 1.557475, -2.083349, 0.711706, 0.139695, -0.105899, 0.522153, -1.262495, -2.782576, 2.278375, -0.311963, 1.556499, -0.583394, 0.633369, 0.790345, -0.598467, 1.243132, -0.955706, 
+-0.276913, -1.537763, 1.102310, -0.816991, 0.944117, 0.350149, -0.518151, -0.129856, -2.120938, -1.056584, -0.589614, 0.224708, 1.465348, 1.443088, 1.137617, 2.256113, -0.924560, 0.192418, -0.632853, 0.193750, 0.676127, 0.890469, -1.324645, 0.833387, 0.715228, 0.088871, 0.354731, -0.894514, -0.836639, 1.178043, -1.846778, 0.311547, 1.725544, 1.072861, -0.646338, -2.383934, -0.979799, -1.792971, -0.476271, 0.417970, -0.484739, -1.627786, -0.320832, 0.732635, 0.069517, 1.146422, 1.098313, -1.068801, 
+-0.412911, 0.015702, -0.952685, 1.300952, 0.251893, 0.187002, 0.088522, 1.665739, 0.343693, 0.696304, 0.774528, -0.965451, 0.158482, 0.062025, -2.146026, 0.688516, -0.898788, -0.578635, 1.797422, -0.150791, -0.394565, 1.189476, -1.224870, -1.778126, -1.147893, -1.171269, -0.780080, -0.515124, -0.958307, -0.799200, 0.556863, -0.208384, -2.042026, 0.783921, 0.610801, -0.968387, -0.777982, -0.052149, -1.603191, 1.300185, 1.604945, 0.627929, -0.152670, 1.610359, 0.125751, 0.663835, -1.231250, -0.130418, 
+0.970202, -1.562104, 0.739450, 1.205480, -1.513343, -0.950236, 0.799201, 1.273865, -0.178462, -0.388660, -0.705930, -0.773205, 2.256703, -0.676248, -1.251304, 1.069394, -1.178837, -0.334270, -1.154090, 1.177038, 0.344436, -0.451133, 1.441097, 0.107797, 1.670370, 1.708682, -1.461428, -1.555964, -1.337666, -0.475169, -1.439023, -0.125849, -1.659160, 0.611661, 0.104008, 0.733054, 0.773528, -0.687255, 0.161889, -0.374641, -0.241549, 1.408682, -2.272800, 0.230105, 0.842431, 0.413574, 0.400499, -1.045114, 
+1.132277, 0.175882, -0.831024, -0.549042, -0.405825, 0.984179, 1.845832, -1.044061, -0.754816, -0.629633, 0.377282, 0.340406, -0.861429, -1.129370, 1.188513, 1.226438, -0.715993, 0.382879, 0.084133, -0.500453, 0.106935, -0.859303, 0.539714, 0.207862, -1.155193, 0.691276, 1.026561, 1.135437, 0.134692, -1.658056, 0.182844, -0.910474, -0.595207, -1.278484, 0.028596, -0.996991, -1.177703, 1.689902, 0.130803, -1.290201, -0.484373, -0.867855, 0.071132, -0.229201, -1.352147, 0.686102, -0.410713, 0.649567, 
+-0.068990, 1.150358, -1.752799, 0.262646, 1.308519, 0.132123, 1.689442, 0.661829, -1.456692, 0.214446, 2.167406, 0.322303, -1.229846, 0.503296, -1.164086, 2.612462, -0.511489, -1.143208, 0.181770, 0.954308, 0.136055, 1.768401, 1.832775, 0.626703, -1.794761, 0.233417, -0.092318, 2.139232, -2.161617, 0.954481, 0.653538, -0.435218, 0.699482, -0.880610, -0.144665, 0.878739, -0.278592, 1.433549, -1.421176, -0.661481, 0.008683, 0.209311, -0.209377, 1.383398, -0.422567, 0.172916, -1.318021, 0.574422, 
+1.648516, -0.794175, 0.050829, -1.392110, 0.333827, 0.007823, -0.780941, 0.165654, 0.108668, -0.675880, -1.704031, 0.500753, 2.314500, -1.200230, -0.516426, 1.175876, -2.729020, -1.422966, 1.588657, 0.158670, -0.054893, -0.501941, -0.270696, 0.953520, -0.058950, -0.536710, 0.239999, 0.580033, 0.213092, 1.693337, 0.573686, -0.993929, 0.651211, -0.169092, 0.848509, 0.631773, -0.323756, -0.416181, 0.801532, 0.267096, -0.355543, 1.479185, 0.989713, 1.787562, 1.371827, -0.146418, -0.775021, 0.039294, 
+-0.143106, -1.393044, 0.729489, 0.805061, 0.532527, 1.110953, 0.457528, 0.516815, 0.185332, 0.084175, 2.175503, -2.514298, -2.636159, -0.119683, 0.476382, 1.207823, 2.706793, -0.173495, -0.452169, -0.301030, -1.100591, 1.436898, -1.544416, -0.947960, -0.828458, 1.272432, -1.067012, -0.063938, -2.300635, -0.867870, 0.164208, 0.096259, -1.681947, 1.774622, -0.039361, 0.953327, 1.447953, -0.543702, 0.546656, -0.621001, 0.029002, 1.123420, -0.678565, -0.475772, 0.247536, -0.305408, 1.358983, 0.008659, 
+1.086786, 0.809736, -1.107911, -0.024606, -0.781897, -1.195758, -0.215365, 2.042416, -0.382569, 0.854137, -0.735513, 0.337939, -0.593979, 1.201335, -0.512881, -0.403624, -1.599635, 0.520721, -0.240726, -0.023926, 1.081919, -0.458000, -0.725997, 1.737847, 0.420936, 0.433443, -0.209354, 0.466891, 0.820243, 2.574957, -1.332407, 0.533745, -1.012075, -0.418343, 0.396640, -0.770129, -0.209679, -0.237824, 0.270201, 0.186531, 0.923184, 1.634915, 1.005538, -1.006746, -1.283855, 0.260582, 0.067053, 0.073514, 
+3.066159, -1.686890, -1.394869, -0.991302, -0.729312, 0.543902, -0.522531, -0.531127, -2.048716, -1.222974, -0.203579, 1.162480, 0.882083, -1.411804, -0.999025, 0.225945, 1.025301, 0.477981, 0.401587, -3.362888, -0.708000, 0.879134, 0.629743, 0.211071, -1.054376, -0.319103, -0.666548, 0.270454, -0.723588, -1.136060, -1.428204, -0.897900, 1.498445, -0.824573, -0.159437, -1.708405, -0.921864, -0.224240, -0.805493, -0.055547, -0.366517, 1.064944, -0.940013, -0.207914, 0.179423, 0.199083, -1.566305, 1.351952, 
+1.054878, 0.927320, 1.401778, -1.402371, -0.278063, -0.521980, 0.212321, -0.782230, -1.504734, -0.770825, -2.419491, -0.107284, 2.644902, 0.280714, -0.193287, -2.018058, 0.771756, 1.567445, 0.660095, 0.048403, -1.161527, 0.860082, -0.254965, -2.054029, -0.960239, 0.558347, 2.053740, -1.027634, 0.211320, 0.061989, 0.619911, 0.762082, -0.741537, -0.771457, 0.504455, 2.159199, -0.281117, 0.230427, -2.962783, -1.076597, -0.528443, 0.765412, -0.638338, 0.564368, -1.230826, 0.093669, 1.515959, 0.079805, 
+-0.554617, -0.468013, -0.247934, -0.618640, -1.013872, -0.410232, 0.507090, -0.227457, -1.710413, -0.429237, 0.122698, -1.148173, -1.583558, 0.327218, -0.552314, 2.407473, -0.996565, -0.047047, 0.911699, 1.103712, 0.781409, -0.929592, 0.346897, 0.435205, 1.404565, 0.471965, 1.522178, 0.398041, -0.428214, 0.324873, 0.334860, 1.276306, 0.157035, -0.364861, -1.413251, -1.361258, -0.028965, -0.827634, -1.077576, 0.266139, -0.634145, -1.286516, 3.437581, -0.136603, 0.448859, -1.129084, 0.540304, 0.659225, 
+-0.739504, 0.411587, 1.566798, 0.476443, -1.002026, -1.737379, 0.465573, 0.376598, 0.585847, 0.049739, 0.261440, 0.051147, -0.264067, -1.129181, -0.814202, -0.896600, 0.841810, 0.577379, 0.661100, 1.361011, 0.184780, -0.416964, 0.939900, -1.681895, -0.892885, -0.102168, 0.241934, 0.068586, 0.542730, 0.210186, -0.516278, 1.084731, 1.812801, 1.601110, -2.300474, -1.223096, -0.023037, 0.108196, 0.083601, 1.152334, 1.985726, 1.216500, 1.051149, 1.134167, 0.472556, -0.261963, 0.313344, 0.171354, 
+1.246304, 0.877761, 0.990020, 0.137354, 0.076433, -1.068594, -0.851166, -0.971745, 0.671022, -1.610473, 0.768979, -0.769391, -0.870306, -1.116085, -0.244580, 1.302315, 0.557150, -0.498758, -0.347456, -0.060117, -0.417689, -0.042969, -0.226866, 1.014861, 2.082572, -1.734965, -0.627782, -0.673211, 1.231489, 1.136193, -0.900954, -0.770066, -3.249496, 0.771564, -0.971411, 0.465394, 1.467712, 0.172622, -0.222064, 2.522313, -0.047311, 0.662510, 0.011539, -2.221314, 0.015368, 0.361208, -1.981613, -1.008825, 
+-0.567395, 0.428741, 1.139569, -0.355586, 0.775178, 1.710090, -1.073190, -1.289464, 1.328457, -1.079843, 0.374956, 0.632344, 0.889936, 0.633631, -0.678139, -0.597587, -1.767240, 0.696470, -2.196051, -1.253323, -0.117394, 1.732937, 0.957731, -0.114314, 0.502618, -1.563038, 0.305790, 0.762349, -0.459681, 0.412588, -0.045099, -0.252700, -0.144508, 0.201776, -1.667386, -1.473770, 1.086134, 0.768623, -0.186824, -2.577099, -1.269826, -1.288777, -0.360374, 1.000156, -1.404388, 0.415587, -0.681224, -0.408812, 
+0.190918, 0.353454, 0.669854, 0.361229, -1.126444, 1.045916, -0.084995, -1.393381, 0.928429, -1.407386, -0.845098, 1.989947, -0.031975, 1.373466, 1.040363, -0.398487, -0.317649, -0.191092, 0.938941, -0.577373, -0.806188, -0.693730, 0.923304, 0.147027, 1.436895, -0.116946, 0.597092, -0.009099, -0.829717, -0.823330, -1.429803, 0.123242, -2.567524, -1.361123, 0.674992, 0.138170, 1.677820, 0.242770, 0.898629, -0.371087, 1.300035, 0.311927, -1.099729, 1.394745, 0.789813, 0.442633, -1.895830, 0.233968, 
+
+ };
+static float combinedBMatrix1[] = {0.466970,-0.771688,-0.196361,-0.361755,-1.564999,-0.733580,0.681487,1.829485,-1.706388,-1.343395,1.382563,0.112830,-0.596906,0.030298,-0.398034,-0.385064,};
+
+void initFastRNN_test0() {
+    fastrnnParams_test0.timeSteps = 8;
+    fastrnnParams_test0.featLen = 32;
+    fastrnnParams_test0.statesLen = 32;
+    fastrnnParams_test0.W = combinedWMatrix0;
+    fastrnnParams_test0.b = combinedBMatrix0;
+    fastrnnParams_test0.alpha = 0.200000;
+    fastrnnParams_test0.beta = 0.800000;}
+
+void initFastRNN_test1() {
+    fastrnnParams_test1.timeSteps = 8;
+    fastrnnParams_test1.featLen = 32;
+    fastrnnParams_test1.statesLen = 16;
+    fastrnnParams_test1.W = combinedWMatrix1;
+    fastrnnParams_test1.b = combinedBMatrix1;
+    fastrnnParams_test1.alpha = 0.100000;
+    fastrnnParams_test1.beta = 0.900000;
+}
+
+unsigned testFastRNN(){
+    initFastRNN_test0();
+    initFastRNN_test1();
+    unsigned errorCode = 0;
+    struct SFastRNNParams2 sparams;
+    unsigned statesLen0 = fastrnnParams_test0.statesLen;
+    unsigned timeSteps1 = fastrnnParams_test1.timeSteps;
+    unsigned statesLen1 = fastrnnParams_test1.statesLen;
+    float h0container[statesLen0 * timeSteps1];
+    memset(h0container, 0, (statesLen0 * timeSteps1) * sizeof(float));
+    initSFastRNN2(&sparams, &fastrnnParams_test0, &fastrnnParams_test1,
+            h0container);
+
+    float result_h[statesLen1];
+    float xx0[] = {
+        -0.056191, 0.266785, 0.706131, -0.056590, 0.554194, 0.069645, 0.383606, -0.389710, -1.358090, -0.612110, 2.015233, -0.044182, -1.281251, -1.973740, -0.132684, -0.128783, 1.665753, -0.369606, 0.015513, -0.510067, 2.518297, 0.931629, 1.339843, 1.421740, 2.989314, -1.462043, -1.110043, -2.864840, -0.527357, -0.646060, -0.159178, -0.075092, 
+-0.511312, 0.942434, 0.873762, 0.116588, -0.983042, -0.824800, 1.671912, -0.672275, -0.609243, 0.252794, -1.381035, 0.730032, -0.839169, -0.694773, -0.595064, 0.326903, 0.461285, -0.042619, -0.058203, -0.554638, -0.946376, -1.076252, 0.144527, 0.356507, -1.250440, -1.214351, 2.645912, -1.017114, 1.400434, -0.645703, -0.748585, 0.873188, 
+1.212197, -0.125025, -1.293777, -0.344142, 2.584050, 0.580884, -0.567701, 0.929973, 0.010716, 0.524903, -0.121728, 0.661749, 0.410242, -0.483042, 0.861600, -0.328118, 0.048788, -0.298288, -1.492886, -0.360744, 1.424788, 1.879074, -1.169187, 0.590654, 0.038874, 0.165297, 1.246526, -1.756052, 0.514656, 1.510233, 2.199108, 1.011497, 
+1.348039, -0.441414, 0.137613, -1.196835, -0.152186, -0.229677, 0.722746, 1.132448, -0.896995, -1.159950, -0.661169, -0.110333, 0.664205, -1.210022, 0.547283, -1.104366, 0.469959, -1.465336, -0.160547, -0.887215, -2.848070, -1.570448, 0.467960, 1.138154, 1.354482, -0.795970, -0.286402, -0.464551, -0.132590, 1.130615, 1.818592, -0.197847, 
+-0.646041, 1.578235, -0.116582, -0.790907, -1.155138, -0.831059, -0.267613, -0.611765, 0.684839, 0.615221, -0.614833, -1.122117, -0.506492, 0.141640, 0.325319, 1.921321, -0.067482, 1.005864, 0.198167, -0.402632, -0.507546, 1.194380, 1.284281, 0.385342, 0.358281, 2.052834, 0.951517, -0.538102, 0.521491, -1.276650, -0.964844, 0.320279, 
+-0.937566, 0.217689, 0.911070, -1.064220, 0.681396, -0.800930, -0.320392, 0.578763, -0.073558, -1.211973, -0.686864, 1.849235, -1.631959, 0.430338, 0.720432, 0.455635, -0.254185, 1.086024, -1.872478, 0.750074, -1.377284, 0.005351, -1.726048, 0.075303, -0.052587, 0.177886, 0.591844, 1.572575, -0.397068, 0.755427, -0.506161, 0.937822, 
+-0.399813, 1.448343, 0.831667, 0.791065, -0.865595, -0.432023, -0.781083, 0.989580, 0.035696, -0.447442, 0.173184, -1.752484, 3.614840, 1.656095, -1.001761, 1.767076, 0.946025, 0.628080, -0.053687, 2.777987, 1.190513, -0.148970, 1.947460, -0.703608, 1.885385, -0.606568, -1.019255, 0.247924, 1.519970, -1.366303, 0.292728, -0.242215, 
+-0.662965, 0.879210, 0.926663, -0.224315, 0.301045, -0.390883, -0.423224, -0.138460, -0.208352, 0.983078, -0.503910, -0.761392, -0.071659, -0.089766, 0.113785, -0.844891, -1.869581, -0.635512, 0.558501, 0.395873, 0.831143, -0.028959, 0.574552, 0.832641, 1.019656, -1.818322, -1.043085, 0.212568, -0.105201, 0.471247, -0.427681, -0.596025, 
+
+    };
+    float xx1[] = {
+        1.231635, 1.117833, -0.535560, 0.577192, 0.086548, -0.408542, -0.430586, -0.735620, -0.823585, -0.004089, 0.986747, 0.560486, -0.718779, -0.841740, -0.581637, -0.439233, -0.418329, -0.449660, -0.558783, -0.784975, -0.748499, -0.187717, -0.319766, -1.063744, -0.482510, -0.545512, 0.148539, -0.042982, -0.093486, 0.309774, 1.687298, 1.877211, 
+0.477761, -0.939763, -1.242839, -2.161232, 1.064679, -0.835279, -0.649859, -0.218253, 0.384495, -0.433703, 0.779523, -0.405676, 1.015064, 0.021128, 0.375293, 0.374084, -0.274300, 0.430880, 0.815998, -0.628664, 0.222751, 0.884704, 1.669474, 0.637315, 0.090367, -1.867354, 2.666825, -1.437411, -1.344906, -0.584182, 1.386705, -0.727339, 
+0.658600, -0.025635, -0.047930, -1.711524, -1.252732, 0.156793, 1.044597, -0.437792, 0.696837, 0.309204, -0.001138, 1.954136, 0.974782, 1.085600, 0.784824, 0.300919, -0.494417, -0.762364, 2.795276, -0.169030, 0.987404, 0.328369, 0.222949, 1.888095, 0.298006, -0.618815, -0.330635, 0.389719, -0.625286, 1.551743, 1.579035, 0.311115, 
+-0.794383, -0.219263, 0.501646, 0.652492, -0.021700, -0.017374, 0.148159, -0.549022, -0.820080, -0.415760, 0.364196, 0.668794, -0.889112, 0.701957, 0.002044, -0.502291, 0.992860, 0.473977, 2.107175, 0.611529, -0.438716, -0.704469, 0.790908, -1.255312, -0.945506, 0.464973, -1.221311, -0.394176, 0.075003, -1.243079, 0.640658, 2.338804, 
+0.553327, 0.394777, 0.599887, 0.120523, -0.022111, -0.116350, -0.091410, -1.108693, -0.654499, -0.620681, -0.006712, -0.714101, 0.276542, -0.706556, 0.412113, -0.453296, -1.228978, 1.965989, 1.690243, -0.390909, 0.954920, 1.207894, 0.865035, -0.147056, 1.304161, -0.512061, 0.459172, -1.176981, 0.683283, 0.807899, 0.197983, -1.020839, 
+-1.424000, -1.523446, 0.589045, -0.173144, -0.501268, 0.108729, 0.102308, 2.156228, -0.270203, 1.140540, 0.379084, -1.355044, -0.570825, 0.989790, 0.204872, 0.533278, 1.554431, -0.427193, -0.365658, -1.638875, -0.537831, -0.426361, -0.252062, 0.670814, -0.147235, 0.232785, -0.433275, 0.540248, 0.205646, -0.711538, 0.330528, 0.928032, 
+0.264745, -0.805613, -0.342389, -0.383660, 1.300838, -2.229253, -0.576488, 1.049650, -0.191567, 0.434514, -1.069150, -0.748220, -0.739352, 1.488635, -0.309497, -0.056896, 0.991565, 1.098724, -1.167169, 0.932995, -1.529572, -0.326587, -0.257983, -0.550167, 0.416134, -0.368677, 1.101769, -1.339027, -0.873228, -0.960299, 0.086369, 0.145524, 
+-1.765507, -1.221685, 0.976221, 0.645661, -1.419126, 1.171028, -0.596129, -0.254297, -0.517926, 0.389857, 0.032958, -0.151349, -1.201180, -0.509303, -0.337875, -0.382813, 0.923180, -0.629485, -0.808807, 0.753967, -0.753527, 1.240845, 0.840624, 0.111097, -0.212108, -0.366454, 0.444949, -0.078091, -0.732064, -0.014033, 0.174081, -0.262851, 
+
+    };
+    for(int i = 0; i < timeSteps1; i++)
+        SFastRNNInference2(&sparams, xx0, result_h);
+    unsigned long StartTime = millis();
+    for(int i = 0; i < 100; i++)
+        SFastRNNInference2(&sparams, xx1, result_h);
+    unsigned long CurrentTime = millis();
+    unsigned long ElapsedTime = CurrentTime - StartTime;
+    Serial.println("Time taken for 100 runs (ms): ");
+    Serial.println(ElapsedTime);
+    Serial.print("Layer 0 [");
+    Serial.print("Inp: "); Serial.print(fastrnnParams_test0.featLen);
+    Serial.print(" hDim: "); Serial.print(statesLen0);
+    Serial.print(" ts: "); Serial.print(fastrnnParams_test0.timeSteps);
+    Serial.println("]");
+    Serial.print("Layer 1 [");
+    Serial.print("Inp: "); Serial.print(fastrnnParams_test1.featLen);
+    Serial.print(" hDim: "); Serial.print(statesLen1);
+    Serial.print(" ts: "); Serial.print(fastrnnParams_test1.timeSteps);
+    Serial.println("]");
+    return errorCode;
+}
+
+void setup() {
+    Screen.init();
+    Serial.begin(115200);
+}
+
+void loop() {
+    Serial.println("Starting");
+    testFastRNN();
+    delay(1000);
+}
+
+int main(){
+    setup();
+    delay(500);
+    for(int i = 0; i < 100; i++)
+        loop();
+}
+
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn_pipeline/generateSFastRNNParams.py b/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn_pipeline/generateSFastRNNParams.py
new file mode 100644
index 000000000..7ed12f44d
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn_pipeline/generateSFastRNNParams.py
@@ -0,0 +1,124 @@
+import numpy as np
+from template_MXChip import getTemplate as getTemplateMXChip
+import python_speech_features as sp
+
+np.random.seed(42)
+
+def sigmoid(x):
+    return 1.0 / (1 + np.exp(-x).astype(np.float32))
+
+class FastRNN:
+    def __init__(self, W, B, alpha, beta, timeSteps, featLen, statesLen):
+        self.timeSteps = timeSteps
+        self.featLen = featLen
+        self.statesLen = statesLen
+        self.alpha = alpha
+        self.beta =  beta
+        self.W = W
+        self.B = B
+
+    def cell(self, x, h):
+        W = self.W
+        B = self.B
+        hx = np.concatenate([h, x])
+        hcomb = np.matmul(W, hx)
+        h_ = hcomb  + B
+        h_ = sigmoid(h_)
+        h_ = self.alpha * h_ + self.beta * h
+        return h_
+
+    def unroll(self, x_list):
+        h = np.zeros(self.statesLen)
+        for x in x_list:
+            h = self.cell(x, h)
+        return h
+
+
+def main(device='C'):
+    assert device in ['C', 'MXChip']
+    audioLen = 16000
+    inputDim = 32
+    hiddenDim0 = 16
+    timeSteps0 = 8
+    hiddenDim1 = 16
+    timeSteps1 = 8
+    alpha0, beta0 = 0.2, 0.8
+    alpha1, beta1 = 0.1, 0.9
+    numOutput = 6
+
+    # Define the FastRNN model
+    # Replace with actual model and data later.
+    # -----------------------------------------
+    preemph = 0.97
+    W0 = np.random.normal(size=[hiddenDim0, hiddenDim0 +  inputDim]) * 0.1
+    B0 = np.random.normal(size=hiddenDim0) * 0.01
+    W1 = np.random.normal(size=[hiddenDim1, hiddenDim1 + hiddenDim0])
+    B1 = np.random.normal(size=hiddenDim1)
+    fastrnn0 = FastRNN(W0, B0, alpha0, beta0, timeSteps0,
+        inputDim, hiddenDim0)
+    fastrnn1 = FastRNN(W1, B1, alpha1, beta1,
+        timeSteps1, hiddenDim0, hiddenDim1)
+    fcW = np.random.normal(size=[numOutput, hiddenDim1])
+    fcB = np.random.normal(size=[numOutput])
+    audioSamples = (np.random.normal(size=audioLen) * 2000).astype(int)
+    # -----------------------------------------
+
+    # Create the template
+    ret = getTemplateMXChip(W0, B0, alpha0, beta0, W1, B1, alpha1, beta1,
+                timeSteps0, timeSteps1, fcW, fcB, audioSamples, numOutput)
+    fp = open('testdata.h', 'w+')
+    print(ret, file=fp)
+    # Check if framing is correct
+    frames = sp.sigproc.framesig(audioSamples, frame_len=400, frame_step=160)
+    tt = audioSamples[1:] - preemph * audioSamples[:-1]
+    tt = np.append(audioSamples[0], tt)
+    pre_frames = sp.sigproc.framesig(tt, frame_len=400, frame_step=160)
+
+    for i in range(5):
+        tt = frames[i]
+        # print("input", tt[:5])
+        # print("pr-ed input", pre_frames[i, :5])
+        ttt = np.zeros(512)
+        ttt[:len(tt)] = pre_frames[i, :]
+        cfft = np.fft.fft(ttt, n=512)
+        # print("fft", cfft[:5])
+        # print()
+    logfbank, energy = sp.fbank(audioSamples, nfilt=32, preemph=preemph)
+    logfbank = np.log(logfbank)
+    for feat in logfbank[:10]:
+        # print(feat[:5])
+        pass
+
+    # run fastRNN0 on the input for each brick and 
+    # collect all the hidden satates 0
+    h0_list = []
+    h1_list = []
+    logits_list = []
+    input1List = []
+    for i in range(0, len(logfbank), timeSteps0):
+        if i + timeSteps0 > len(logfbank):
+            continue
+        input0 = logfbank[i:i+timeSteps0]
+        # print(input0[7, -10:])
+        assert len(input0) == timeSteps0
+        h = fastrnn0.unroll(input0)
+        # print('--> %2d' % (i / timeSteps0), h)
+        h0_list.append(h)
+        input1List.append(h)
+        assert len(input1List) <= timeSteps1
+        if len(input1List) == timeSteps1:
+            input1 = np.array(input1List)
+            assert len(input1) == timeSteps1
+            assert input1.shape[1] == hiddenDim0
+            h = fastrnn1.unroll(input1)
+            h1_list.append(h)
+            logits = np.matmul(fcW, h) + fcB
+            logits_list.append(logits)
+            print("--> h1   ", h)
+            print("--> fcout", logits)
+            print()
+            del input1List[0]
+
+
+device = 'MXChip'
+main(device)
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn_pipeline/template_MXChip.py b/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn_pipeline/template_MXChip.py
new file mode 100644
index 000000000..0e18c58b9
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn_pipeline/template_MXChip.py
@@ -0,0 +1,117 @@
+def getTemplate(W0, B0, alpha0, beta0, W1, B1, alpha1, beta1, timeSteps0,
+                timeSteps1, fcW, fcB, x0, numOutput):
+    statesLen0 = len(B0)
+    featLen0 = W0.shape[1] - statesLen0
+    statesLen1 = len(B1)
+    featLen1 = W1.shape[1] - statesLen1
+    assert W0.shape[0] == statesLen0
+    assert W1.shape[0] == statesLen1
+    assert featLen1 == statesLen0
+    assert x0.ndim == 1
+    assert fcW.shape[0] == numOutput
+    assert fcW.shape[1] == statesLen1
+    assert fcW.ndim == 2
+    assert fcB.ndim == 1
+    assert fcB.shape[0] == numOutput
+
+    W0Str = ''
+    for i in range(W0.shape[0]):
+        for j in range(W0.shape[1]):
+            W0Str += '%f, ' % W0[i][j]
+        W0Str += '\n'
+
+    B0Str = ''
+    for i in range(B0.shape[0]):
+        B0Str += '%f,' % B0[i]
+
+    W1Str = ''
+    for i in range(W1.shape[0]):
+        for j in range(W1.shape[1]):
+            W1Str += '%f, ' % W1[i][j]
+        W1Str += '\n'
+
+    B1Str = ''
+    for i in range(B1.shape[0]):
+        B1Str += '%f,' % B1[i]
+
+    FCWStr = ''
+    for i in range(fcW.shape[0]):
+        for j in range(fcW.shape[1]):
+            FCWStr += '%f, ' % fcW[i][j]
+        FCWStr += '\n'
+
+    FCBStr = ''
+    for i in range(fcB.shape[0]):
+        FCBStr += '%f,' % fcB[i]
+
+    xx0Str = ''
+    for i in range(x0.shape[0]):
+        xx0Str += '%d, ' % x0[i]
+        if i % 10 == 0:
+            xx0Str += '\n'
+
+    return    '''
+#include <sfastrnn.h>
+#include <fc.h>
+#include <arm_math.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+struct FastRNNParams fastrnnParams_test0;
+struct FastRNNParams fastrnnParams_test1;
+struct FCParams fcParams_test;
+
+void initFastRNN_test0();
+void initFastRNN_test1();
+void initFC_test();
+
+#ifdef __cplusplus
+}
+#endif
+static float combinedWMatrix0[] = {
+    %s
+ };
+
+static float combinedBMatrix0[] = {%s};
+
+static float combinedWMatrix1[] = {
+    %s
+ };
+static float combinedBMatrix1[] = {%s};
+
+static float fcW[] = {%s};
+static float fcB[] = {%s};
+
+void initFastRNN_test0() {
+    fastrnnParams_test0.timeSteps = %d;
+    fastrnnParams_test0.featLen = %d;
+    fastrnnParams_test0.statesLen = %d;
+    fastrnnParams_test0.W = combinedWMatrix0;
+    fastrnnParams_test0.b = combinedBMatrix0;
+    fastrnnParams_test0.alpha = %f;
+    fastrnnParams_test0.beta = %f;}
+
+void initFastRNN_test1() {
+    fastrnnParams_test1.timeSteps = %d;
+    fastrnnParams_test1.featLen = %d;
+    fastrnnParams_test1.statesLen = %d;
+    fastrnnParams_test1.W = combinedWMatrix1;
+    fastrnnParams_test1.b = combinedBMatrix1;
+    fastrnnParams_test1.alpha = %f;
+    fastrnnParams_test1.beta = %f;
+}
+
+void initFC_test(){
+    fcParams_test.W = fcW;
+    fcParams_test.B = fcB;
+    fcParams_test.inputDim = %d;
+    fcParams_test.outputDim = %d;
+}
+
+#define TEST_AUDIO_LEN %d
+static int16_t test_audio[TEST_AUDIO_LEN] = { %s
+};
+''' % (W0Str, B0Str, W1Str, B1Str, FCWStr, FCBStr, timeSteps0, featLen0,
+       statesLen0, alpha0, beta0, timeSteps1, featLen1, statesLen1, alpha1,
+       beta1, statesLen1, numOutput, len(x0), xx0Str)
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn_pipeline/test_sfastrnn2_pipeline.cpp b/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn_pipeline/test_sfastrnn2_pipeline.cpp
new file mode 100644
index 000000000..dbbd46855
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn_pipeline/test_sfastrnn2_pipeline.cpp
@@ -0,0 +1,116 @@
+#include <sfastrnnpipeline.h>
+#include <testdata.h>
+#include <Arduino.h>
+
+extern struct FastRNNParams fastrnnParams_test0;
+extern struct FastRNNParams fastrnnParams_test1;
+extern struct FCParams fcParams_test;
+extern void initFastRNN_test0();
+extern void initFastRNN_test1();
+extern void initFC_test();
+
+#ifdef DEBUG_MODE
+#define PRECISION 4
+void printVoid(void *val){
+    int32_t a = *((int32_t*)val);
+    Serial.println(a);
+}
+
+void printStr(char *a){
+    Serial.print(a);
+}
+
+void printInt32(int32_t val){
+    Serial.println(val);
+}
+
+void printFloatAddr(float *a){
+    Serial.printf("%p\n", a);
+}
+
+void printHexQ31(q31_t val){
+    char buff[20];
+    sprintf(buff, "%p", *(int*)&val);
+   Serial.println(buff);
+}
+
+void printFloatArrF32(float32_t *arr, int len, float scale){
+    for(int i = 0; i < len; i++){
+        float val = ((float*)arr)[i];
+        Serial.print(val * 1.0, PRECISION); Serial.print(", ");
+    }
+    Serial.println();
+    delay(1000);
+}
+
+void printIntArr(int32_t *arr, int len, int offset){
+    for(int i = 0; i < len; i++){
+        int32_t val = arr[i + offset];
+        Serial.print(val); Serial.print(", ");
+    }
+    Serial.println();
+    delay(1000);
+}
+
+void printFloatArrQ31(q31_t *arr, int len, float scale){
+    for(int i = 0; i < len; i++){
+        float32_t val;
+        arm_q31_to_float(&arr[i], &val, 1);
+        Serial.print((float)val * scale, PRECISION); Serial.print(", ");
+    }
+    Serial.println();
+    delay(1000);
+}
+#endif // DEBUG_MODE
+
+void setup(){
+    Serial.begin(115200);
+    Screen.init();
+    delay(500);
+    initFastRNN_test0();
+    initFastRNN_test1();
+    initFC_test();
+    Serial.println();
+    Serial.println("Ready");
+    Screen.print(1, "Ready");
+    delay(500);
+    Screen.clean();
+    unsigned ret = sfastrnn2p_init(&fastrnnParams_test0,
+        &fastrnnParams_test1, &fcParams_test);
+    Serial.printf("Return code: %d (init)\n", ret);
+    if(ret != 0)
+    error("Shallow FastRNN initialization failed (code %d)", ret);
+    if(ret != 0) while(1);
+
+    int32_t test[11] = {2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12};
+}
+
+void loop(){
+    Serial.printf("New Loop\n");
+    Screen.print(1, "New Loop");
+    unsigned ret = 0;
+    // Push 160 samples every 10ms
+    for(int i = 0; i < 100; i++){
+        ret = sfastrnn2p_add_new_samples(&test_audio[i * 160], 160);
+        ret = ret | ret;
+        wait_ms(10);
+    }
+    // Wait for the consumer to finish
+    delay(1000);
+    Serial.printf("Return code: %d (push)\n", ret);
+    Screen.clean();
+    delay(500);
+}
+
+int main(){
+    setup();
+    delay(500);
+    Serial.println("NEW");
+    delay(500);
+    for(int i = 0; i < 25; i++)
+        loop();
+    sfastrnn2p_quit();
+    delay(500);
+    Screen.print("Done");
+}
+
diff --git a/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn_pipeline/testdata.h b/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn_pipeline/testdata.h
new file mode 100644
index 000000000..e49dbefdb
--- /dev/null
+++ b/Applications/KeywordSpotting/MXChip-SRNN/test/time_sfastrnn_pipeline/testdata.h
@@ -0,0 +1,1701 @@
+
+#include <sfastrnn.h>
+#include <fc.h>
+#include <arm_math.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+struct FastRNNParams fastrnnParams_test0;
+struct FastRNNParams fastrnnParams_test1;
+struct FCParams fcParams_test;
+
+void initFastRNN_test0();
+void initFastRNN_test1();
+void initFC_test();
+
+#ifdef __cplusplus
+}
+#endif
+static float combinedWMatrix0[] = {
+    0.049671, -0.013826, 0.064769, 0.152303, -0.023415, -0.023414, 0.157921, 0.076743, -0.046947, 0.054256, -0.046342, -0.046573, 0.024196, -0.191328, -0.172492, -0.056229, -0.101283, 0.031425, -0.090802, -0.141230, 0.146565, -0.022578, 0.006753, -0.142475, -0.054438, 0.011092, -0.115099, 0.037570, -0.060064, -0.029169, -0.060171, 0.185228, -0.001350, -0.105771, 0.082254, -0.122084, 0.020886, -0.195967, -0.132819, 0.019686, 0.073847, 0.017137, -0.011565, -0.030110, -0.147852, -0.071984, -0.046064, 0.105712, 
+0.034362, -0.176304, 0.032408, -0.038508, -0.067692, 0.061168, 0.103100, 0.093128, -0.083922, -0.030921, 0.033126, 0.097555, -0.047917, -0.018566, -0.110633, -0.119621, 0.081253, 0.135624, -0.007201, 0.100353, 0.036164, -0.064512, 0.036140, 0.153804, -0.003583, 0.156464, -0.261975, 0.082190, 0.008705, -0.029901, 0.009176, -0.198757, -0.021967, 0.035711, 0.147789, -0.051827, -0.080849, -0.050176, 0.091540, 0.032875, -0.052976, 0.051327, 0.009708, 0.096864, -0.070205, -0.032766, -0.039211, -0.146351, 
+0.029612, 0.026106, 0.000511, -0.023459, -0.141537, -0.042065, -0.034271, -0.080228, -0.016129, 0.040405, 0.188619, 0.017458, 0.025755, -0.007445, -0.191877, -0.002651, 0.006023, 0.246324, -0.019236, 0.030155, -0.003471, -0.116868, 0.114282, 0.075193, 0.079103, -0.090939, 0.140279, -0.140185, 0.058686, 0.219046, -0.099054, -0.056630, 0.009965, -0.050348, -0.155066, 0.006856, -0.106230, 0.047359, -0.091942, 0.154993, -0.078325, -0.032206, 0.081352, -0.123086, 0.022746, 0.130714, -0.160748, 0.018463, 
+0.025988, 0.078182, -0.123695, -0.132046, 0.052194, 0.029698, 0.025049, 0.034645, -0.068002, 0.023225, 0.029307, -0.071435, 0.186577, 0.047383, -0.119130, 0.065655, -0.097468, 0.078708, 0.115860, -0.082068, 0.096338, 0.041278, 0.082206, 0.189679, -0.024539, -0.075374, -0.088951, -0.081581, -0.007710, 0.034115, 0.027669, 0.082718, 0.001300, 0.145353, -0.026466, 0.272017, 0.062567, -0.085716, -0.107089, 0.048247, -0.022346, 0.071400, 0.047324, -0.007283, -0.084679, -0.151485, -0.044651, 0.085640, 
+0.021409, -0.124574, 0.017318, 0.038532, -0.088386, 0.015373, 0.005821, -0.114297, 0.035779, 0.056078, 0.108305, 0.105380, -0.137767, -0.093783, 0.051504, 0.051379, 0.051505, 0.385273, 0.057089, 0.113557, 0.095400, 0.065139, -0.031527, 0.075897, -0.077283, -0.023682, -0.048536, 0.008187, 0.231466, -0.186727, 0.068626, -0.161272, -0.047193, 0.108895, 0.006428, -0.107774, -0.071530, 0.067960, -0.073037, 0.021646, 0.004557, -0.065160, 0.214394, 0.063392, -0.202514, 0.018645, -0.066179, 0.085243, 
+-0.079252, -0.011474, 0.050499, 0.086576, -0.120030, -0.033450, -0.047495, -0.065333, 0.176545, 0.040498, -0.126088, 0.091786, 0.212216, 0.103247, -0.151937, -0.048423, 0.126691, -0.070767, 0.044382, 0.077463, -0.092693, -0.005953, -0.324127, -0.102439, -0.025257, -0.124778, 0.163241, -0.143014, -0.044004, 0.013074, 0.144127, -0.143586, 0.116316, 0.001023, -0.098151, 0.046210, 0.019906, -0.060022, 0.006980, -0.038531, 0.011352, 0.066213, 0.158602, -0.123782, 0.213303, -0.195209, -0.015179, 0.058832, 
+0.028099, -0.062270, -0.020812, -0.049300, -0.058936, 0.084960, 0.035702, -0.069291, 0.089960, 0.030730, 0.081286, 0.062963, -0.082900, -0.056018, 0.074729, 0.061037, -0.002090, 0.011733, 0.127766, -0.059157, 0.054710, -0.020219, -0.021768, 0.109878, 0.082542, 0.081351, 0.130548, 0.002100, 0.068195, -0.031027, 0.032417, -0.013014, 0.009700, 0.059516, -0.081822, 0.209239, -0.100602, -0.121419, 0.115811, 0.079166, 0.062412, 0.062835, -0.001225, -0.089725, 0.007580, -0.067716, 0.097512, -0.014706, 
+-0.082550, -0.032139, 0.041293, -0.056372, -0.082222, 0.024369, 0.024497, -0.050694, -0.047104, 0.023205, -0.144808, -0.140746, -0.071844, -0.021345, 0.031091, 0.147536, 0.085766, -0.015994, -0.001902, -0.100253, -0.001851, -0.028866, 0.032272, -0.082723, 0.051935, 0.153274, -0.010876, 0.040171, 0.069014, -0.040122, 0.022409, 0.001259, 0.009768, -0.077301, 0.002451, 0.049800, 0.145114, 0.095927, 0.215318, -0.076735, 0.087232, 0.018334, 0.218980, -0.080830, -0.083972, -0.059939, -0.212390, -0.052576, 
+-0.075913, 0.015039, 0.034176, 0.187617, 0.095042, -0.057690, -0.089841, 0.049192, -0.132023, 0.183146, 0.117944, -0.046918, -0.171313, 0.135387, -0.011454, 0.123782, -0.159443, -0.059938, 0.000524, 0.004698, -0.045007, 0.062285, -0.106762, -0.014238, 0.012030, 0.051444, 0.071161, -0.112464, -0.153411, 0.127768, 0.033231, -0.074849, 0.155115, 0.011567, 0.117930, 0.006752, 0.206075, 0.175534, -0.024896, 0.097157, 0.064538, 0.136863, -0.096492, 0.068605, 0.105842, -0.175874, -0.118326, -0.203923, 
+-0.026941, 0.071754, 0.150236, 0.007409, 0.162862, -0.138010, -0.170338, -0.005555, 0.038407, -0.003269, -0.206744, -0.008912, -0.130447, 0.066967, 0.036660, -0.093988, -0.051387, -0.105921, -0.006268, 0.095514, -0.098573, 0.050405, -0.053026, -0.079287, -0.010703, -0.103524, -0.055365, -0.119788, 0.196473, 0.003526, -0.069973, 0.021398, -0.011233, -0.022097, 0.061417, 0.075751, -0.053050, -0.057582, -0.027505, -0.230192, -0.151519, 0.136687, 0.164497, -0.024904, 0.057656, 0.031125, 0.307888, 0.111957, 
+-0.012792, -0.095554, -0.160645, 0.020346, -0.075635, -0.142225, -0.064657, -0.108155, 0.168714, 0.088164, -0.000797, 0.147994, 0.007737, -0.086128, 0.152312, 0.053891, -0.103725, -0.019034, -0.087562, -0.138280, 0.092618, 0.190942, -0.139857, 0.056297, -0.065064, -0.048713, -0.059239, -0.086399, 0.004852, -0.083095, 0.027046, -0.005024, -0.023895, -0.090756, -0.057677, 0.075539, 0.050092, -0.097756, 0.009933, 0.075139, -0.166941, 0.054336, -0.066262, 0.057060, -0.076326, -0.180488, -0.162754, 0.004808, 
+0.025972, -0.090432, 0.063859, -0.166152, -0.006608, -0.121102, -0.065184, 0.004740, -0.086041, -0.038456, 0.100629, -0.057689, 0.083569, -0.112971, 0.052980, 0.144157, -0.247164, -0.079690, 0.057707, -0.020305, 0.037115, -0.060399, 0.008659, -0.015568, 0.116778, 0.025442, 0.033760, -0.041188, -0.048761, -0.043256, 0.039445, -0.042098, 0.028977, 0.207540, 0.087112, -0.032602, 0.120121, -0.040808, -0.203812, -0.100809, -0.187079, -0.035151, 0.001842, 0.167644, 0.032693, -0.021910, 0.082941, -0.221114, 
+0.023561, 0.077087, -0.147859, 0.114375, 0.033850, -0.041529, 0.063278, 0.227069, 0.018187, 0.024822, -0.045936, -0.084984, 0.083034, -0.085608, 0.007157, -0.047766, 0.047898, 0.033366, 0.103754, -0.051002, -0.026987, -0.097876, -0.044429, 0.037730, 0.075699, -0.092217, 0.086961, 0.135564, 0.041343, 0.187680, -0.077379, -0.124465, -0.177872, 0.149604, 0.065437, -0.005558, 0.027997, -0.112549, 0.244575, 0.012922, 0.010939, 0.072577, 0.048101, 0.022388, -0.079047, 0.047147, 0.188202, 0.134542, 
+0.159319, -0.051122, -0.098960, -0.012579, 0.005572, 0.109419, -0.169246, 0.152955, -0.015801, -0.042688, -0.101210, -0.165486, 0.082317, 0.007332, -0.128996, -0.129508, -0.033578, 0.166902, -0.025959, -0.150314, -0.024574, -0.027272, -0.269689, -0.005429, -0.023093, 0.069621, 0.184896, 0.112657, -0.026889, -0.110653, 0.257336, 0.005922, 0.001393, -0.002413, 0.019808, -0.014436, -0.057366, -0.054686, -0.003275, -0.054342, -0.071285, 0.010643, -0.025498, 0.150399, -0.265097, 0.109151, 0.124609, -0.207339, 
+-0.034269, -0.037144, -0.140751, -0.077782, -0.111058, 0.175227, 0.093568, 0.127156, 0.072167, -0.112905, -0.052452, 0.048937, -0.122213, 0.071300, -0.024033, -0.037482, 0.071096, 0.044426, -0.036097, 0.115933, -0.108106, 0.061594, 0.059310, -0.030955, 0.032613, -0.125111, 0.092403, -0.018490, -0.052272, 0.104901, -0.070434, -0.140846, -0.155663, 0.060601, -0.128043, 0.175479, -0.208193, 0.169646, 0.021102, -0.009671, -0.054492, 0.039914, -0.003763, 0.110330, 0.011423, 0.015030, -0.036361, -0.005695, 
+0.030780, -0.171017, -0.134819, 0.074326, 0.017087, -0.018398, 0.001843, 0.034758, -0.053976, -0.077830, 0.019585, -0.097837, 0.040825, -0.170258, 0.102916, 0.047260, 0.025603, 0.098269, 0.166547, 0.101437, -0.184087, -0.127958, -0.062482, 0.002609, 0.051766, -0.072574, 0.018677, -0.075538, -0.061152, -0.140666, -0.092323, -0.135168, -0.097587, 0.105364, -0.094940, 0.263238, 0.049332, 0.018484, -0.085836, 0.070031, -0.057564, 0.012201, 0.256008, -0.009606, 0.114927, -0.070318, -0.003499, 0.177080, 
+
+ };
+
+static float combinedBMatrix0[] = {-0.006270,0.018124,0.007078,-0.005625,0.006324,0.009726,0.006218,-0.015702,-0.007271,-0.002475,-0.000744,0.006207,0.001777,-0.013353,0.003802,0.006106,};
+
+static float combinedWMatrix1[] = {
+    0.559790, 1.080781, 0.833922, 0.459180, -0.070166, -1.660961, 0.429618, 0.207688, 0.271579, -1.276749, -1.081057, 1.053153, -0.039555, 0.681501, 0.028318, 0.029756, 0.938284, -0.516045, 0.096121, -0.462275, -0.434496, -0.309172, 0.222134, -0.478749, 1.255756, -0.894607, -0.186872, -0.439731, 1.446978, 0.196555, 1.031845, -1.485560, 
+0.267050, 0.889631, 0.082284, 1.065480, -0.517288, 1.409347, 2.298898, -0.362839, -0.445503, 1.453384, 1.579572, -0.522860, -0.420187, -0.281785, -1.344451, -0.918652, -1.004141, -0.767798, -0.034685, 0.234215, 1.550500, -0.998354, 0.984322, -0.213989, -0.049464, 0.674819, -1.122722, 0.382410, 0.166452, 0.492451, 0.289169, 2.455300, 
+-0.637740, -0.530997, -0.623141, -0.555477, -0.637387, 1.189017, 1.420504, -0.570746, -0.832356, 0.471416, -0.552223, 0.632932, 0.202923, -1.515744, 1.547505, 1.795878, -0.612789, -0.387702, 0.285865, 0.334457, 0.658544, 2.010205, -0.176947, -0.798297, -1.379319, -0.730930, -0.033127, 1.794558, -0.517611, 0.223788, -0.016423, 1.188393, 
+2.526932, -0.530869, -0.489439, 1.044161, 0.681891, 1.846707, 0.583928, -0.359292, 0.590655, 1.108704, 0.820482, 0.507274, 1.066675, 1.169296, 1.382159, 0.648710, -0.167118, 0.146714, 1.206509, -0.816936, 0.368673, -0.393339, 0.028745, 1.278452, 0.191099, 0.046437, -1.359856, 0.746254, 0.645484, 2.163255, -0.307778, 0.219150, 
+0.249384, 1.577453, -0.095296, 0.279022, 0.607897, 0.186609, -0.446434, 0.194090, 1.073632, -1.026515, 0.132970, -0.700121, 1.195047, -1.523187, -0.558922, 0.377212, 1.565524, -0.065750, -0.555200, 1.881157, -1.448014, -2.198806, 0.440014, -0.502054, -1.021233, 0.708356, 0.243801, -0.564079, -1.280304, 0.872457, 0.650201, -0.099176, 
+1.846637, -1.070085, -1.525525, -0.691908, -0.045586, 0.243339, -0.241236, 0.352055, -1.251539, 1.443765, -0.082151, 1.117296, 0.342725, 0.456753, 0.569767, 0.447709, 0.642723, 1.329153, 0.196521, 0.709004, -0.089736, 1.440117, -0.676392, 1.800940, -0.040158, -1.430775, 0.128104, -0.681052, 0.840644, -0.652624, -0.446183, -1.889541, 
+-0.452306, -2.423879, -1.583903, 0.760415, 0.785800, 0.425458, -0.966976, -0.047711, -0.003603, -1.158365, 1.503398, 0.877362, -0.220964, 0.026886, 0.208383, -2.041735, -0.247177, -0.681984, -1.001620, -0.281100, 1.797687, 0.640843, -0.571179, 0.572583, 1.399355, 0.924634, 0.059630, -0.646937, 0.698223, 0.393485, 0.895193, 0.635172, 
+1.049553, -0.535235, 1.317394, 0.197600, 2.075261, -0.689188, 1.735964, 0.197911, -0.651418, -0.483886, -0.320347, 0.424166, 0.522835, -0.573700, -0.024355, 2.142270, 1.727543, 0.436324, 0.038003, 0.120031, 0.613518, -1.022793, -0.257377, -1.668584, 0.399223, 0.647196, -0.483186, 1.573987, -1.225766, -1.464375, 0.224452, 1.047098, 
+1.683928, -0.458884, 1.078681, -0.038508, -0.172627, 0.883660, 0.652323, -1.576392, 1.476540, 1.380091, -0.625563, 0.395804, 0.494030, 0.260674, -0.550305, -0.671623, -0.025554, 1.172729, 0.543600, -0.370614, 0.771699, -2.848543, 1.148766, -1.739714, -0.362441, -1.119670, -1.294681, 1.160827, -0.467701, 0.346504, -0.046921, 0.477041, 
+0.076822, -1.282992, 0.996267, -0.493757, -1.556582, -0.428115, 1.500760, 0.850222, -0.348652, -0.349258, -0.321635, 2.076748, 0.381935, 0.430042, 1.030283, 0.238789, -0.259042, -0.196350, -0.071601, -0.037222, 0.727630, 0.051946, 0.732640, -0.080717, 0.078635, -1.998201, 0.916328, 0.346488, 0.998010, -2.896255, 2.088375, -0.139590, 
+1.108183, -1.039906, 0.612774, -1.053416, -0.623769, 1.914031, -0.190682, 0.217433, 0.870068, 0.495682, 0.150419, 0.364961, 2.403416, -0.057619, 0.201099, 1.050654, 1.105526, 1.187030, 0.638730, -1.143005, 1.633432, -1.146345, 0.302635, -0.754276, -0.064138, 0.328762, 0.321357, 0.421921, 1.613711, 0.453534, -0.244157, 0.964087, 
+1.189470, -1.227608, 0.597400, 0.701173, -0.297564, 1.375707, -0.150056, 0.125576, -0.173072, 0.015579, -1.096275, -1.440051, 1.594505, -0.846961, -0.991392, -2.153390, -0.638962, -1.323090, 1.642015, 1.009817, -0.688150, 2.252436, 0.981765, -0.324831, -2.499406, 2.290943, -1.389572, -1.645399, 1.022570, 2.439752, 1.384273, 0.563909, 
+0.594754, 0.853416, 0.758929, 0.281191, 0.104201, -0.062593, -0.753965, -0.280675, -1.692957, -0.098340, -0.988591, -1.103589, 0.179894, 1.392002, 0.918317, -1.570501, -0.989628, 0.940771, -0.982487, -0.224633, 0.550052, -0.968344, 0.105376, -1.334025, -0.601368, 0.319782, -1.592994, 0.440475, -0.019638, 0.552490, 0.223914, 1.364140, 
+0.125225, -0.429406, 0.122298, 0.543298, 0.048860, 0.040592, -0.701992, -0.662901, -1.402605, 1.749577, -1.243863, -0.692905, -0.718407, 0.894924, -0.294950, 1.247742, -0.673491, 0.278994, -0.835347, 2.145149, -1.187598, 0.309821, 0.633777, 0.413799, -0.185288, -0.129821, 0.043811, -0.147002, 0.963879, 2.210523, -0.557492, -1.369803, 
+-0.088282, 2.579709, -0.803675, 1.639117, 1.677701, -0.553588, 0.568983, 1.628397, -0.379128, -0.203580, -0.581681, -1.014757, -0.649278, -1.223940, 0.034083, -0.769973, 0.233786, -1.555896, 0.330880, 0.833529, -1.993736, 0.374057, 1.227669, -1.209641, 1.672572, 0.419019, -0.705012, -0.055769, 0.558327, 0.076005, 0.538756, -0.920674, 
+0.169361, -1.413714, -0.111226, -0.903908, -0.735530, 1.236093, 1.091310, 0.609138, -1.092313, -0.316408, 1.213098, 0.141717, 2.319330, 0.393318, 0.192049, -0.309116, 0.133541, -0.152470, 0.708109, 0.956702, -0.785989, -1.331233, -1.836205, 0.507991, -1.103367, -2.152891, 0.388579, 2.493000, -0.006071, 0.838491, 0.081829, -0.098890, 
+
+ };
+static float combinedBMatrix1[] = {0.919076,-0.290275,0.267392,0.321698,-0.668090,0.992042,-0.174960,-0.755745,0.536510,-0.898468,0.028181,-0.009119,1.085896,0.474698,-0.025027,0.817766,};
+
+static float fcW[] = {1.390208, 0.557810, 0.010353, -1.311836, -1.065114, -0.305225, -0.609512, -0.186971, 0.056650, 0.529693, -0.070499, 0.486502, 0.064474, -1.975467, -0.939335, -0.144088, 
+-1.209695, 0.599929, 1.530751, 1.218762, -0.213443, 1.490726, 0.148667, -0.337086, -0.613403, -0.302470, -0.388177, 0.170416, 0.160574, 0.003046, 0.436938, 1.190646, 
+0.949554, -1.484898, -2.553921, 0.934320, -1.366879, -0.224765, -1.170113, -1.801980, 0.541463, 0.759155, -0.576510, -2.591042, -0.546244, 0.391804, -1.478912, 0.183360, 
+-0.015310, 0.579291, 0.119580, -0.973069, 1.196572, -0.158530, -0.027305, -0.933268, -0.443282, -0.884803, -0.172946, 1.711708, -1.371901, -1.613561, 1.471170, -0.209324, 
+-0.669073, 1.039905, -0.605616, 1.826010, 0.677926, -0.487911, 2.157308, -0.605715, 0.742095, 0.299293, 1.301741, 1.561511, 0.032004, -0.753418, 0.459972, -0.677715, 
+2.013387, 0.136535, -0.365322, 0.184680, -1.347126, -0.971614, 1.200414, -0.656894, -1.046911, 0.536653, 1.185704, 0.718953, 0.996048, -0.756795, -1.421811, 1.501334, 
+};
+static float fcB[] = {-0.322680,-0.250833,1.328194,0.556230,0.455888,2.165002,};
+
+void initFastRNN_test0() {
+    fastrnnParams_test0.timeSteps = 8;
+    fastrnnParams_test0.featLen = 32;
+    fastrnnParams_test0.statesLen = 16;
+    fastrnnParams_test0.W = combinedWMatrix0;
+    fastrnnParams_test0.b = combinedBMatrix0;
+    fastrnnParams_test0.alpha = 0.200000;
+    fastrnnParams_test0.beta = 0.800000;}
+
+void initFastRNN_test1() {
+    fastrnnParams_test1.timeSteps = 8;
+    fastrnnParams_test1.featLen = 16;
+    fastrnnParams_test1.statesLen = 16;
+    fastrnnParams_test1.W = combinedWMatrix1;
+    fastrnnParams_test1.b = combinedBMatrix1;
+    fastrnnParams_test1.alpha = 0.100000;
+    fastrnnParams_test1.beta = 0.900000;
+}
+
+void initFC_test(){
+    fcParams_test.W = fcW;
+    fcParams_test.B = fcB;
+    fcParams_test.inputDim = 16;
+    fcParams_test.outputDim = 6;
+}
+
+#define TEST_AUDIO_LEN 16000
+static int16_t test_audio[TEST_AUDIO_LEN] = { -1287, 
+1855, 114, 537, 3056, 1015, 1076, 2145, -729, -1678, -2089, 
+-3932, 4112, -2206, -442, -553, 614, 1631, 1720, -1166, -334, 
+565, -497, 3214, 981, 1469, 1325, 2346, 362, -2593, 799, 
+-1302, -1057, 1172, 2476, 42, 617, 3404, 481, 5203, 1131, 
+-3521, 1506, 762, 2579, 1346, -276, -2448, -418, -1701, -1161, 
+1177, 3339, 789, -2391, 889, 2393, -1219, -268, 29, -1569, 
+1296, -241, 839, -1774, -874, 1444, -745, 3453, -799, 449, 
+1865, -2836, -3521, -3051, 2525, -1103, 5116, -1128, 369, 3084, 
+4012, 4123, 2416, 2048, 1185, 1556, -1102, -1636, -6, -340, 
+-906, 1392, 1910, 176, 2955, -2283, -387, -1433, -3733, -165, 
+-243, 3026, 1261, -2048, 3708, 2442, 1164, -452, -1918, -744, 
+2177, 3769, 3086, -977, -2239, 281, -3536, 646, -295, -932, 
+-3189, 1027, -1065, -2339, -5744, -55, 3544, 3322, -914, -1204, 
+937, -1996, 603, 1532, 2453, -200, -407, -1755, -1653, -452, 
+734, 1827, -1606, 2985, -542, -42, -1494, -4848, 1768, 1473, 
+-562, 133, 1031, -3125, -1058, 1588, -2508, 587, -2713, 932, 
+-71, -3230, 2329, -1469, -1620, 401, 2297, -2031, 123, 857, 
+1386, 352, -734, -1655, 172, -2144, -5842, 873, 1807, -4725, 
+-2019, 1238, 4114, 41, -1456, -365, 2749, -1291, -1598, -965, 
+-1906, 245, 3249, 646, -504, -583, -3126, 1766, -155, -360, 
+6386, 597, -1503, -852, 2296, 226, -2876, 1838, -1336, 3746, 
+2160, -894, 2562, 135, 1705, 969, -1692, -1287, 2059, -669, 
+-807, -1910, 847, 4125, -2135, 48, 2824, -159, 904, -2124, 
+856, -374, 1971, 2374, 5179, 1159, 651, 388, -706, 676, 
+-590, 336, 2635, -2013, 2279, 2634, -236, -4243, -1215, 2593, 
+-45, -1998, -1009, 1681, 1093, -477, -733, -783, -1844, 3230, 
+-644, 2434, 3042, 1996, -863, 807, -48, -1807, 648, -2358, 
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+-482, 1345, 3799, -265, -1949, 2214, -240, -4345, 1694, -1070, 
+-181, 663, 380, 1418, -870, 1026, -519, 1477, 1230, -1870, 
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+-3232, -2107, -2135, 1900, 3421, -208, -337, 140, 2323, -1854, 
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+2747, -274, 1905, 3224, 2629, 3279, 1484, 150, -3203, -492, 
+-1686, 4341, -351, 246, 1102, 87, 3390, -1245, 389, -1484, 
+-2640, -1223, -74, -858, -1384, -2812, -166, -3009, 1520, 164, 
+-2915, -618, -1504, 638, 2680, -3750, 230, -320, 1342, 426, 
+-1503, -638, -1592, 2152, 42, 3802, -121, -1416, -3027, -3606, 
+-3168, 534, 1017, -3162, 1790, -966, 293, 3224, 1793, -537, 
+-1782, -4303, -1438, -422, -1974, -262, 153, -449, -1300, 337, 
+883, -2180, 2821, -197, 37, 1416, 466, 1906, 574, -1224, 
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+-1838, -5060, -572, 2201, 3916, -2459, 993, -930, -211, 5288, 
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+-387, -529, -4007, 1270, -2478, 119, 554, 2721, -2617, -6039, 
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+-1026, -457, -1988, -5124, -382, 4825, 1569, -38, -525, 44, 
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+-1569, -741, -2811, 31, 1804, -1815, 3038, 1021, 2061, -1315, 
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+-2734, 1696, -2465, 1104, 1251, -1393, 1164, 520, -1077, -2017, 
+-3925, 699, -3129, 190, -526, 1358, -604, -658, 1463, 670, 
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