#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
Code for MobiCom paper 'TinyML-CAM: 80 FPS Image Recognition in 1 Kb RAM'
Neural Networks with low bit weights on low end 32 bit microcontrollers such as the CH32V003 RISC-V Microcontroller and others
#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
TensorFlow Lite models for MIRNet for low-light image enhancement.