An Efficient Pipeline for Pruning Convolutional Neural Networks
Chengcheng Li, Zi Wang, Hairong Qi · 2020
Network pruning has achieved significant success in compressing and accelerating CNNs. However, the existing three-step iterative pipeline, which includes ranking, pruning, and fine-tuning, is extremely computationally expensive due to the feed-forward and back-propagation operations conducted in both the ranking and fine-tuning steps. In this paper, we present a computationally efficient framework for structured pruning by exploring the potential of leveraging the intermediate results generated during the fine-tuning step to rank the importance of filters and thus converting a three-step pipeline (with precise ranking) to a two-step pipeline (with coarse ranking), resulting in significant savings in computation time while achieving comparable performance in terms of classification accuracy as compared to the state-of-the-art. The proposed method is evaluated with various benchmark architectures and datasets for the image classification task. Experimental results show that the proposed approach can achieve superior performance in computation efficiency while maintaining the same accuracy level. Our approach would largely facilitate pruning practice, especially on resource-constrained platforms.