Introduction to Machine Learning Systems

Lightweight inference library for ONNX files, written in C++. It can run Stable Diffusion XL 1.0 on a RPI Zero 2 (or in 298MB of RAM) but also Mistral 7B on desktops and servers. ARM, x86, WASM, RISC-V supported. Accelerated by XNNPACK.

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

A lightweight header-only library for using Keras (TensorFlow) models in C++.

This is a list of interesting papers and projects about TinyML.

[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning; [NeurIPS 2022] MCUNetV3: On-Device Training Under 256KB Memory

The Fastest Deep Reinforcement Learning Library

Machine Learning inference engine for Microcontrollers and Embedded devices

vendor independent TinyML deep learning library, compiler and inference framework microcomputers and micro-controllers

[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. 🔥🔥🔥

Instructions, source code, and misc. resources needed for building a Tiny ML-powered artificial nose.

Zant simplifies the deployment and optimization of neural networks on microprocessors

Neural Networks with low bit weights on low end 32 bit microcontrollers such as the CH32V003 RISC-V Microcontroller and others

Code for MobiCom paper 'TinyML-CAM: 80 FPS Image Recognition in 1 Kb RAM'

Notes on Machine Learning on edge for embedded/sensor/IoT uses

In this repository you will find TinyML course syllabi, assignments/labs, code walkthroughs, links to student projects, and lecture videos (where applicable).

This is the TinyML programs for ESP32 according to BlackWalnut Labs Tutorials. (黑胡桃实验室的TinyML教程中的程序集合)

A research library for pytorch-based neural network pruning, compression, and more.

Rune provides containers to encapsulate and deploy edgeML pipelines and applications