Structured Model Pruning of Convolutional Networks on Tensor Processing Units
Abstract
The deployment of convolutional neural networks is often hindered by high computational and storage requirements. Structured model pruning is a promising approach to alleviate these requirements. Using the VGG-16 model as an example, we measure the accuracy-efficiency trade-off for various structured model pruning methods and datasets (CIFAR-10 and ImageNet) on Tensor Processing Units (TPUs). To measure the actual performance of models, we develop a structured model pruning library for TensorFlow2 to modify models in place (instead of adding mask layers). We show that structured model pruning can significantly improve model memory usage and speed on TPUs without losing accuracy, especially for small datasets (e.g., CIFAR-10).
Cite
@article{arxiv.2107.04191,
title = {Structured Model Pruning of Convolutional Networks on Tensor Processing Units},
author = {Kongtao Chen and Ken Franko and Ruoxin Sang},
journal= {arXiv preprint arXiv:2107.04191},
year = {2021}
}
Comments
International Conference on Machine Learning 2021 Workshop on Overparameterization: Pitfalls & Opportunities