For detailed installation and usage guidelines, please refer to this project's documentation
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.
torchapprox was created by Elias Trommer. It is licensed under the terms of the MIT license.
torchapproxwas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.- 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
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},
}