Machine Learning Systems

[ICLR 2020] Once for All: Train One Network and Specialize it for Efficient Deployment

Z80-μLM is a 2-bit quantized language model small enough to run on an 8-bit Z80 processor. Train conversational models in Python, export them as CP/M .COM binaries, and chat with your vintage computer.

Machine Learning inference engine for Microcontrollers and Embedded devices

[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning

Seeed SenseCraft Model Assistant is an open-source project focused on embedded AI. 🔥🔥🔥

Building Simple versions of AI (ML, DL, NN) models from scratch to help grasp the concepts

DNN Compiler for Heterogeneous SoCs

SensiML's open-source AutoML solution for Edge AI model development

TinyML & Edge AI: On-device inference, model quantization, embedded ML, ultra-low-power AI for microcontrollers and IoT devices.

Source code generators for machine learning models.

Code for WF-IoT paper 'TinyML Benchmark: Executing Fully Connected Neural Networks on Commodity Microcontrollers'

[Tiny VAD] SG-VAD: Stochastic Gates Based Speech Activity Detection

A ternary, zero-heap tiny language model that runs inside a $2 microcontroller — bit-exact Python <-> C99 <-> Cortex-M3 (QEMU) parity. Apache-2.0.

Perform AI-based sleep monitoring

Jamming Detection in IoT Wireless Networks using TinyML -- Code and Dataset

Auditable offline edge intelligence for low-cost edge devices, with benchmark evidence and public board proof on ESP32-C3.

🌠 Enhanced Network Compression Through Tensor Decompositions and Pruning

[Tiny KWS] SparkNet: Sparse Binarization for Fast Keyword Spotting

A tool to support using classification models in low-power and microcontroller-based embedded systems.