#Tinyml
Showing 17 of 17 repositories tagged #tinyml, ranked by stars
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. Python, C# and JS(WASM) bindings available.
This is a list of interesting papers and projects about TinyML.
The Fastest Deep Reinforcement Learning Library
[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
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
vendor independent TinyML deep learning library, compiler and inference framework microcomputers and micro-controllers
Notes on Machine Learning on edge for embedded/sensor/IoT uses
Code for MobiCom paper 'TinyML-CAM: 80 FPS Image Recognition in 1 Kb RAM'
A carefully curated collection of high-quality libraries, projects, tutorials, research papers, and other essential resources focused on TinyML — the intersection of machine learning and ultra-low-power embedded systems.
A ternary, zero-heap tiny language model that runs inside a $2 microcontroller — bit-exact Python <-> C99 <-> Cortex-M3 (QEMU) parity. Apache-2.0.
On-device AI recipes for building private, real-time applications with production-grade voice, language, and vision SDKs. Ready-to-use, open-source code examples.
Spying on Microcontrollers using Current Sensing and embedded TinyML models
Neural-Kalman GNSS/INS Navigation for Precision Agriculture
TinyOdom: Hardware-Aware Efficient Neural Inertial Navigation
The TinyML "Hello World" sine wave model on Arduino Uno v3