Combination pruning method based on reinforcement learning and 3σ criterion

In order to resolve the problem that deep neural network with complex structure and redundant parameters could not be deployed to the resource constrained embedded system, an efficient combination pruning method based on reinforcement learning and 3σ criterion was proposed, which was inspired by the effect of sparsity rate on performance. Firstly, an optimal global sparsity rate was determined according to the influence of sparsity rate on accuracy, which could achieve a good balance between sparsity rate and accuracy. Secondly, under the guidance of optimal global sparsity rate, the reinforcement learning method was used to search the optimal pruning rate of each convolutional layer automatically, and the unimportant weights were cut off on the basis of the pruning rate. Then, the weight pruning threshold of each fully connected layer was determined by 3σ criterion, and for each fully connected layer, the weight which below the threshold would be pruned. Finally, the accuracy of model recognition was restored by retraining. Experimental results showed that the proposed pruning method could compress the parameters of VGG16, ResNet56 and ResNet50 network by 83.33%, 70.1% and 80.9% respectively, and the model’s recognition accuracy could be reduced by 1.55%, 1.98% and 1.86% respectively. © 2023 Zhejiang University. All rights reserved.