onnx2c
tinyengine
| onnx2c | tinyengine | |
|---|---|---|
| 1 | 3 | |
| 166 | 742 | |
| - | 3.4% | |
| 6.6 | 5.6 | |
| about 1 month ago | about 1 month ago | |
| C | C | |
| GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
onnx2c
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[D] Run Pytorch model inference on Microcontroller
onnx2c - onnx to c sourcecode converter. Looks interesting, but also not very active.
tinyengine
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[D] Run Pytorch model inference on Microcontroller
TinyEngine from MCUNet. Looks great, targeting ARM CM4.
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[D][R] Deploying deep models on memory constrained devices
However, I am looking on this subject through the problem of training/finetuning deep models on the edge devices, being increasingly available thing to do. Looking at tflite, alibaba's MNN, mit-han-lab's tinyengine etc..
- TinyEngine: High-performance neural network library for Microcontrollers
What are some alternatives?
deepC - vendor independent TinyML deep learning library, compiler and inference framework microcomputers and micro-controllers
prnf - A lightweight tiny printf alternative. With some reasonable limitations, extensions, and alternative behaviour suited to microcontrollers.
ML-examples - Arm Machine Learning tutorials and examples
nnom - A higher-level Neural Network library for microcontrollers.
CMSIS-NN - CMSIS-NN Library
ai8x-synthesis - Quantization and Synthesis (Device Specific Code Generation) for ADI's MAX78000 and MAX78002 Edge AI Devices
EdgeML - This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India.
TinyMaix - TinyMaix is a tiny inference library for microcontrollers (TinyML).
SLID-on-Microcontrollers - Speech Classification using a Convolutional Neural Network running on a Microcontroller