Skip to content

Latest commit

 

History

History
executable file
·
50 lines (37 loc) · 1.95 KB

README.md

File metadata and controls

executable file
·
50 lines (37 loc) · 1.95 KB
Raw

Fast as CHITA: Neural Network Pruning with Combinatorial Optimization

This is the offical repo of the ICML 2023 paper Fast as CHITA: Neural Network Pruning with Combinatorial Optimization

Requirements

This code has been tested with Python 3.7 and the following packages:

numba==0.56.4
numpy==1.21.6
scikit_learn==1.0.2
torch==1.12.1+cu113
torchvision==0.13.1+cu113

Pruned models

We provide checkpoints for our best pruned models, obtained with the gradual pruning procedure described in the paper.

MobileNetV1

Sparsity Checkpoint
75.28 link
89.00 link

ResNet50

Sparsity Checkpoint
90.00 link
95.00 link
98.00 link

Structure of the repo

Scripts to run the algorithms are located in scripts/. The current code supports the following architectures (datasets): MLPNet (MNIST), ResNet20 (Cifar10), MobileNetV1 (Imagenet) and ResNet50 (Imagenet). Adding new models can be done through model_factory function in utils/main_utils.py.

Citing CHITA

If you find CHITA useful in your research, please consider citing the following paper.

@InProceedings{pmlr-v202-benbaki23a,
  title = 	 {Fast as {CHITA}: Neural Network Pruning with Combinatorial Optimization},
  author =       {Benbaki, Riade and Chen, Wenyu and Meng, Xiang and Hazimeh, Hussein and Ponomareva, Natalia and Zhao, Zhe and Mazumder, Rahul},
  booktitle = 	 {Proceedings of the 40th International Conference on Machine Learning},
  pages = 	 {2031--2049},
  year = 	 {2023},
}