Neural Network Pruning by Cooperative Coevolution

Shang，Haopu1; Wu，Jia Liang1; Hong，Wenjing2; Qian，Chao1

2022

1045-0823

IJCAI International Joint Conference on Artificial Intelligence

4814-4820

Neural network pruning is a popular model compression method which can significantly reduce the computing cost with negligible loss of accuracy. Recently, filters are often pruned directly by designing proper criteria or using auxiliary modules to measure their importance, which, however, requires expertise and trial-and-error. Due to the advantage of automation, pruning by evolutionary algorithms (EAs) has attracted much attention, but the performance is limited for deep neural networks as the search space can be quite large. In this paper, we propose a new filter pruning algorithm CCEP by cooperative coevolution, which prunes the filters in each layer by EAs separately. That is, CCEP reduces the pruning space by a divide-and-conquer strategy. The experiments show that CCEP can achieve a competitive performance with the state-of-the-art pruning methods, e.g., prune ResNet56 for 63.42% FLOPs on CIFAR10 with −0.24% accuracy drop, and ResNet50 for 44.56% FLOPs on ImageNet with 0.07% accuracy drop.

Others

English

EI

20223812753694

Deep neural networks

Ergonomics and Human Factors Engineering:461.4

2-s2.0-85136987029

Scopus

1.State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing,210023,China
2.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China

Shang，Haopu,Wu，Jia Liang,Hong，Wenjing,et al. Neural Network Pruning by Cooperative Coevolution[C],2022:4814-4820.