| htqin/awesome-model-quantization |
1,449 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
|
|
| A list of papers, docs, codes about model quantization. This repo is aimed to provide the info for model quantization research, we are continuously improving the project. Welcome to PR the works (papers, repositories) that are missed by the repo. |
| intel/intel-extension-for-pytorch |
1,161 |
|
0 |
12 |
about 2 years ago |
13 |
October 19, 2023 |
180 |
apache-2.0 |
Python |
| A Python package for extending the official PyTorch that can easily obtain performance on Intel platform |
| Xilinx/brevitas |
1,015 |
|
0 |
2 |
about 2 years ago |
13 |
December 08, 2023 |
142 |
other |
Python |
| Brevitas: neural network quantization in PyTorch |
| openppl-public/ppq |
957 |
|
0 |
0 |
almost 3 years ago |
0 |
|
9 |
apache-2.0 |
Python |
| PPL Quantization Tool (PPQ) is a powerful offline neural network quantization tool. |
| Xilinx/finn |
629 |
|
0 |
2 |
about 2 years ago |
5 |
November 04, 2021 |
66 |
bsd-3-clause |
Python |
| Dataflow compiler for QNN inference on FPGAs |
| cedrickchee/awesome-ml-model-compression |
378 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
mit |
|
| Awesome machine learning model compression research papers, tools, and learning material. |
| ysh329/embedded-ai.bi-weekly |
356 |
|
0 |
0 |
almost 4 years ago |
0 |
|
4 |
mit |
|
| WeChat: NeuralTalk，Weekly report and awesome list of embedded-ai. |
| hpi-xnor/BMXNet |
344 |
|
0 |
0 |
over 6 years ago |
0 |
|
0 |
apache-2.0 |
C++ |
| (New version is out: https://github.com/hpi-xnor/BMXNet-v2) BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet |
| sun254/awesome-model-compression-and-acceleration |
329 |
|
0 |
0 |
almost 5 years ago |
0 |
|
3 |
|
|
| a list of awesome papers on deep model ompression and acceleration |
| sony/model_optimization |
245 |
|
0 |
0 |
about 2 years ago |
0 |
|
12 |
apache-2.0 |
Python |
| Model Compression Toolkit (MCT) is an open source project for neural network model optimization under efficient, constrained hardware. This project provides researchers, developers, and engineers advanced quantization and compression tools for deploying state-of-the-art neural networks. |
