[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

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

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

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)

在ESP32上实现基于红外热成像阵列传感器的手势识别

Live Image Classification on ESP32-CAM and TFT with MobileNet v1 from Edge Impulse (TinyML)

Code for IoT Journal paper 'ML-MCU: A Framework to Train ML Classifiers on MCU-based IoT Edge Devices'

CMix-NN: Mixed Low-Precision CNN Library for Memory-Constrained Edge Devices

NNSE (Neural Network Speech Enhancement) is a speech-denoiser optimized to run on Ambiq's low power platform

TensorFlow Lite C/C++ library for microcontrollers.

TinyML for STM32F407 using different frameworks in C and C++ with Keil uVision IDE.

Handwritten number recognition with STMF429 TFlite micro mnist

TinyNS: Platform-Aware Neurosymbolic Auto Tiny Machine Learning

Raspberry Pi Pico (RP2040) playground

Collection of media arts instruments using tiny machine learning, and based on microcontrollers.

Code release for "TinySpeech: Attention Condensers for Deep Speech Recognition Neural Networks on Edge Devices"

Run Machine learning on a microcontroller with an accelerometer sensor to classify different moves with the racket - Forehand, Backhand, Serve, and Idle.

An easy guide for Color classification (RGB) using Multilayer Perceptron (MLP) Neural Networks with Arduino

This repository is dedicated to the second tutorial of my youtube channell MakeIT. This project is about tiny machine learning and how to forecast weather using Arduino Nano 33 BLE Sense.

This project aims to provide a low-cost solution for remote physiotherapy sessions using an Arduino Nano 33 BLE Sense board to recognize gestures made by the user.