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

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

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

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.

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},
}