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GPU-accelerated Neural Network layers using Approximate Multiplications for PyTorch

License: MIT License

Shell 0.21% C++ 3.48% Python 53.86% C 0.88% Cuda 21.07% Jupyter Notebook 20.51%

torch-approx's Introduction

GPU-accelerated Neural Network layers using Approximate Multiplication for PyTorch

1. Documentation

For detailed installation and usage guidelines, please refer to this project's documentation

2. Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

3. License

torchapprox was created by Elias Trommer. It is licensed under the terms of the MIT license.

4. Credits

torchapprox was created with cookiecutter and the py-pkgs-cookiecutter template.
Depthwise Convolution Kernels based on: https://github.com/rosinality/depthwise-conv-pytorch
This work was created as part of my Ph.D. research at Infineon Technologies Dresden

5. Citation

If you use TorchApprox in your work, please cite it as:

@inproceedings{trommer23torchapprox,
  author       = {Elias Trommer and
                  Bernd Waschneck and
                  Akash Kumar},
  editor       = {Maksim Jenihhin and
                  Hana Kub{\'{a}}tov{\'{a}} and
                  Nele Metens and
                  Jaan Raik and
                  Foisal Ahmed and
                  Jan Belohoubek},
  title        = {High-Throughput Approximate Multiplication Models in PyTorch},
  booktitle    = {26th International Symposium on Design and Diagnostics of Electronic
                  Circuits and Systems, {DDECS} 2023, Tallinn, Estonia, May 3-5, 2023},
  pages        = {79--82},
  publisher    = {{IEEE}},
  year         = {2023},
  url          = {https://doi.org/10.1109/DDECS57882.2023.10139366},
  doi          = {10.1109/DDECS57882.2023.10139366},
}

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