摘要
Convolutional neural networks (CNN) have achieved significant success in various domains, e.g., image recognition, however, deploying CNN on resource-constrained devices remains challenging due to their high computation demands and large memory requirements. In this paper, we propose an approach for structured pruning of CNN using genetic algorithms, specifically focusing on channel pruning. The pruning process often results in a loss of accuracy, requiring retraining techniques to recover the network's accuracy to its original level. We integrate knowledge distillation into the retraining process to recover the model to a similar accuracy within fewer epochs. The experimental results show that our approach successfully reduces the amounts of parameters by 91.02% and computation demands by 90.96%, with a 1.82% decrease in accuracy only for a gesture recognition model with over 30 million parameters. The results demonstrate that our approach significantly reduces the computation of the model without dramatically decreasing its accuracy.