TinyMaix
esp-nn
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| TinyMaix | esp-nn | |
|---|---|---|
| 10 | 3 | |
| 825 | 111 | |
| 3.2% | 5.4% | |
| 1.5 | 4.7 | |
| 17 days ago | 7 months ago | |
| C | Assembly | |
| Apache License 2.0 | Apache License 2.0 |
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TinyMaix
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[D] Run Pytorch model inference on Microcontroller
TinyMaix. Very minimalistic, can also be used on RISC-V
- Show HN: PicoVGA Library – VGA/TV Display on Raspberry Pi Pico
- [Discussion] Best practices for taking deep learning models to bare metal MCUs
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Meet Sipeed’s TinyMaix: An Open-Source Lightweight Machine Learning Library For Microcontrollers
Sipeed TinyMaix is an open-source machine learning library designed for microcontrollers. According to findings, it is lightweight enough to be compatible with Microchip ATmega328 MCU found in the Arduino UNO board and its many clones.
- TinyMaix: Enable Deeplearning for embedded device with 1KB SRAM
- TinyMaix: Ultra Lightweight TinyML Infer Lib (using tflite quant strategy)
- TinyMaix: Ultra Lightweight TinyML Infer Lib optimized for RISC-V P/V extend~
- TinyMaix: Enable Deeplearning for IoT device with 1KB SRAM
esp-nn
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TinyML: Ultra-low power Machine Learning
There are a range of ML acceleration possible on existing chips. The basic 4-wide 8 bit integer SIMD extensions in NEON is available on basically all ARM Cortex M4F chips, which is already available 8+ years. It gives 4-5x speedup for neural networks.
The more recent ESP32-S3 has operations with up to 10x speedup, see https://github.com/espressif/esp-nn
Then there are RISCV chips with neural network co processors like Kendryte K210.
ARM has also defined a new set of extensions for NN acceleration, and reference designs for cores being ARM Cortex M85. Chips are becoming available this year.
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[Discussion] Best practices for taking deep learning models to bare metal MCUs
https://github.com/espressif/esp-nn https://github.com/espressif/tflite-micro-esp-examples
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Ask HN: What Are You Working on This Year?
The ESP32-S3 introduces vector instructions which are supported by TFLite already. See here - https://github.com/espressif/esp-nn#performance
S3 also supports Octal PSRAM vs the single lane SPI PSRAM found on the older ESP32-CAM style boards, should be 8x the bandwidth with all else equal. So far I've only seen the octal PSRAM's available on WROOM modules and it looks likes there only support for Espressif branded Octal PSRAM chips at this point.
What are some alternatives?
ai8x-synthesis - Quantization and Synthesis (Device Specific Code Generation) for ADI's MAX78000 and MAX78002 Edge AI Devices
love - LÖVE is an awesome 2D game framework for Lua.
nnom - A higher-level Neural Network library for microcontrollers.
roqr - QR codes that will rock your world
tinyengine - [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
Home Assistant - :house_with_garden: Open source home automation that puts local control and privacy first.
pico-zxspectrum - ZX Spectrum for Raspberry Pico Pi RP2040
bevy - A refreshingly simple data-driven game engine built in Rust
mbot-vision - Let your Makeblock robot see using an ESP32, camera and PyTorch
linux-surface - Linux Kernel for Surface Devices
b-em - An RP2040 and Raspberry Pi 2/3/4/Zero 2 W version of an opensource BBC Micro emulator originally for Win32 and Linux
MaximAI_Documentation - START HERE: Documentation for ADI's MAX78000 and MAX78002 Edge AI devices