[ICLR 2020] Once for All: Train One Network and Specialize it for Efficient Deployment
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Updated
Dec 14, 2023 - Python
[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++.
[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
This is a list of interesting papers and projects about TinyML.
This repository holds the Google Colabs for the EdX TinyML Specialization
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
Lightweight inference library for ONNX files, written in C++. It can run SDXL on a RPI Zero 2 but also Mistral 7B on desktops and servers.
Instructions, source code, and misc. resources needed for building a Tiny ML-powered artificial nose.
In this repository you will find TinyML course syllabi, assignments/labs, code walkthroughs, links to student projects, and lecture videos (where applicable).
Machine Learning inference engine for Microcontrollers and Embedded devices
Seeed SenseCraft Model Assistant is an open-source project focused on embedded AI. 🔥🔥🔥
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)
This repository holds the Arduino Library for the EdX TinyML Specialization
Notes on Machine Learning on edge for embedded/sensor/IoT uses
TensorFlow Lite models for MIRNet for low-light image enhancement.
A research library for pytorch-based neural network pruning, compression, and more.
Code for IoT Journal paper 'ML-MCU: A Framework to Train ML Classifiers on MCU-based IoT Edge Devices'
Code for paper 'Multi-Component Optimization and Efficient Deployment of Neural-Networks on Resource-Constrained IoT Hardware'
Code for MobiCom paper 'TinyML-CAM: 80 FPS Image Recognition in 1 Kb RAM'
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