Post-Training Deep Neural Network Pruning via Layer-Wise Calibration

Ivan Lazarevich, Alexander Kozlov, Nikita Malinin

ICCVW 2021 pp. 798-805

doi:10.1109/ICCVW54120.2021.00094 /iccvw/2021/lazarevich2021iccvw-posttraining/

Abstract

We present a post-training weight pruning method for deep neural networks that achieves accuracy levels tolerable for the production setting and that is sufficiently fast to be run on commodity hardware such as desktop CPUs or edge devices. We propose a data-free extension of the approach for computer vision models based on automatically-generated synthetic fractal images. We obtain state-of-the-art results for data-free neural network pruning, with ~1.5% top@1 accuracy drop for a ResNet50 on ImageNet at 50% sparsity rate. When using real data, we are able to get a ResNet50 model on ImageNet with 65% sparsity rate in 8-bit precision in a post-training setting with a ~1% top@1 accuracy drop. We release the code as a part of the OpenVINOTM Post-Training Optimization tool1.

PDF ICCVW Semantic Scholar

Cite

Text

Lazarevich et al. "Post-Training Deep Neural Network Pruning via Layer-Wise Calibration." IEEE/CVF International Conference on Computer Vision Workshops, 2021. doi:10.1109/ICCVW54120.2021.00094

Markdown

[Lazarevich et al. "Post-Training Deep Neural Network Pruning via Layer-Wise Calibration." IEEE/CVF International Conference on Computer Vision Workshops, 2021.](https://mlanthology.org/iccvw/2021/lazarevich2021iccvw-posttraining/) doi:10.1109/ICCVW54120.2021.00094

BibTeX

@inproceedings{lazarevich2021iccvw-posttraining,
  title     = {{Post-Training Deep Neural Network Pruning via Layer-Wise Calibration}},
  author    = {Lazarevich, Ivan and Kozlov, Alexander and Malinin, Nikita},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2021},
  pages     = {798-805},
  doi       = {10.1109/ICCVW54120.2021.00094},
  url       = {https://mlanthology.org/iccvw/2021/lazarevich2021iccvw-posttraining/}
}