Scaleout Edge: Sovereign Edge AI orchestration and Federated Learning
In this repository, we explore model compression for transformer architectures via quantization. We specifically explore quantization aware training of the linear layers and demonstrate the performance for 8 bits, 4 bits, 2 bits and 1 bit (binary) quantization.
A camera for measuring sediment grain sizes with edge ML
Hyperdimensional Computing and Vector Symbolic Architectures in JAX
An awesome list of "small but mighty" models and resources.
ESP32-S3 WiFi CSI Human Activity Recognition system with real-time RF sensing, ML inference, and live visualization dashboard.
BEAVER automates the Edge AI lifecycle through LLM-powered orchestration. It Builds, Evolves, Analyzes, Validates, Executes, and Repairs—just like beavers in nature, but for edge devices!
Compress PyTorch models for edge devices — CPU-only, no GPU, no retraining. One function call.
embedded software components for event-based application development
Notes and resources from Qualcomm On-device AI course, provided by DeepLearningAI
Edge-deployable keyword spotter: INT8-quantized DS-CNN on Google Speech Commands, exported to ONNX, with fp32 vs INT8 benchmarks, a live mic demo, and a C++ inference harness.
Python ML library for person fall detection. Intended for IoT deployments with on-device inference and on-device transfer learning.
A curated list of machine learning models that run locally on edge devices, including phones, browsers, laptops, Raspberry Pi-class boards, Jetson, Coral, NPUs, and microcontrollers.
A system for monitoring statistical data distribution shifts in distributed settings
Fault-tolerant Edge ML pipeline for space weather (TEC) prediction. Fuses raw ISRO telemetry with NASA APIs for sub-second, on-device inference via quantized TensorFlow Lite.
Lightweight Attention U-Net for Breast Cancer Semantic Segmentation
On-device TinyML on the Silicon Labs Thunderboard Sense 2 (EFR32MG12, no NPU): an 85% IMU gesture recognizer running fully on-chip at ~87.5ms, plus a deep-audited hardware reference set.
Edge-first XRF V2 benchmark for deployable wearable event detection (earbuds + smart-glasses), with FP/hour-calibrated metrics and reproducible run artifacts.
Pre-quantized models for edge inference on Cortex-M, ESP32-S3, and Raspberry Pi. Six models — keyword spotting, person detection, hand-gesture recognition, anomaly detection, voice activity detection, and binary defect classifier — each shipped as TF
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