#计算机科学#Introduction to Machine Learning Systems
#计算机科学#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-...
[ICLR 2020] Once for All: Train One Network and Specialize it for Efficient Deployment
#计算机科学#A lightweight header-only library for using Keras (TensorFlow) models in C++.
#计算机科学#This is a list of interesting papers and projects about TinyML.
#计算机科学#[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 256K...
#计算机科学#The Fastest Deep Reinforcement Learning Library
#计算机科学#Machine Learning inference engine for Microcontrollers and Embedded devices
#计算机科学#vendor independent TinyML deep learning library, compiler and inference framework microcomputers and micro-controllers
#计算机科学#[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning
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'
#计算机科学#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)
#计算机科学#A research library for pytorch-based neural network pruning, compression, and more.
Rune provides containers to encapsulate and deploy edgeML pipelines and applications