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Merge branch 'feature_axi_stream' into develop
2 parents 0ecae52 + 1b486f0 commit 8746cf7

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‎examples/thread_stream_axi_stream_fifo_ipxact/Makefile

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TARGET=$(shell ls *.py | grep -v test | grep -v parsetab.py)
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ARGS=
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PYTHON=python3
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#PYTHON=python
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#OPT=-m pdb
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#OPT=-m cProfile -s time
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#OPT=-m cProfile -o profile.rslt
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.PHONY: all
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all: test
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.PHONY: run
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run:
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$(PYTHON) $(OPT) $(TARGET) $(ARGS)
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.PHONY: test
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test:
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$(PYTHON) -m pytest -vv
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.PHONY: check
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check:
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$(PYTHON) $(OPT) $(TARGET) $(ARGS) > tmp.v
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iverilog -tnull -Wall tmp.v
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rm -f tmp.v
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.PHONY: clean
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clean:
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rm -rf *.pyc __pycache__ parsetab.py .cache *.out *.png *.dot tmp.v uut.vcd

‎examples/thread_stream_axi_stream_fifo_ipxact/run_on_pynq.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/javascript": [
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"\n",
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"require(['notebook/js/codecell'], function(codecell) {\n",
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" codecell.CodeCell.options_default.highlight_modes[\n",
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" 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n",
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" Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n",
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" Jupyter.notebook.get_cells().map(function(cell){\n",
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" if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n",
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" });\n",
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"});\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"from pynq import Overlay, allocate\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"dict_keys(['axi_dma', 'blinkled_0'])"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"bitfile = \"thread_stream_axi_stream_fifo_ipxact.bit\"\n",
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"overlay = Overlay(bitfile)\n",
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"overlay.ip_dict.keys()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"dma = overlay.axi_dma\n",
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"blinkled = overlay.blinkled_0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"reduce_size = 8\n",
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"read_size = 1024\n",
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"write_size = read_size // reduce_size\n",
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"\n",
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"src = allocate(shape=(read_size,), dtype=np.int32)\n",
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"dst = allocate(shape=(write_size,), dtype=np.int32)\n",
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"bias = allocate(shape=(write_size,), dtype=np.int32)\n",
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"\n",
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"bias_addr = bias.physical_address"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
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]
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}
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],
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"source": [
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"src[:] = np.arange(read_size, dtype=np.int32)\n",
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"dst[:] = np.zeros([write_size], dtype=np.int32)\n",
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"bias[:] = np.ones([write_size], dtype=np.int32)\n",
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"print(dst[-16:])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"dma.sendchannel.transfer(src)\n",
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"dma.recvchannel.transfer(dst)\n",
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"\n",
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"# read_size, write_size, reduce_size, offset\n",
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"blinkled.write(2 * 4, read_size)\n",
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"blinkled.write(3 * 4, write_size)\n",
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"blinkled.write(4 * 4, reduce_size)\n",
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"blinkled.write(5 * 4, bias_addr)\n",
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"\n",
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"# start\n",
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"blinkled.write(0 * 4, 1)\n",
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"\n",
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"# busy wait\n",
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"while True:\n",
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" busy = blinkled.read(1 * 4)\n",
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" if not busy:\n",
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" break"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[6472845 6588493 6705165 6822861 6941581 7061325 7182093 7303885 7426701\n",
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" 7550541 7675405 7801293 7928205 8056141 8185101 8315085]\n"
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]
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}
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],
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"source": [
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"print(dst[-16:])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[6472845 6588493 6705165 6822861 6941581 7061325 7182093 7303885 7426701\n",
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" 7550541 7675405 7801293 7928205 8056141 8185101 8315085]\n"
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]
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}
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],
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"source": [
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"expected = np.sum(np.multiply(src, src).reshape([-1, reduce_size]), axis=-1) + bias\n",
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"print(expected[-16:])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0\n"
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]
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}
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],
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"source": [
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"diff_sum = np.sum(expected - dst)\n",
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"print(diff_sum)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎examples/thread_stream_axi_stream_fifo_ipxact/run_on_pynq.py

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# coding: utf-8
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# In[1]:
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from pynq import Overlay, allocate
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import numpy as np
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# In[2]:
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bitfile = "thread_stream_axi_stream_fifo_ipxact.bit"
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overlay = Overlay(bitfile)
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overlay.ip_dict.keys()
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# In[3]:
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dma = overlay.axi_dma
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blinkled = overlay.blinkled_0
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# In[4]:
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reduce_size = 8
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read_size = 1024
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write_size = read_size // reduce_size
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src = allocate(shape=(read_size,), dtype=np.int32)
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dst = allocate(shape=(write_size,), dtype=np.int32)
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bias = allocate(shape=(write_size,), dtype=np.int32)
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bias_addr = bias.physical_address
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# In[5]:
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src[:] = np.arange(read_size, dtype=np.int32)
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dst[:] = np.zeros([write_size], dtype=np.int32)
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bias[:] = np.ones([write_size], dtype=np.int32)
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print(dst[-16:])
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# In[6]:
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dma.sendchannel.transfer(src)
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dma.recvchannel.transfer(dst)
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# read_size, write_size, reduce_size, offset
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blinkled.write(2 * 4, read_size)
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blinkled.write(3 * 4, write_size)
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blinkled.write(4 * 4, reduce_size)
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blinkled.write(5 * 4, bias_addr)
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# start
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blinkled.write(0 * 4, 1)
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# busy wait
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while True:
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busy = blinkled.read(1 * 4)
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if not busy:
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break
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# In[7]:
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print(dst[-16:])
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# In[8]:
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expected = np.sum(np.multiply(src, src).reshape([-1, reduce_size]), axis=-1) + bias
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print(expected[-16:])
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# In[9]:
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diff_sum = np.sum(expected - dst)
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print(diff_sum)
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