Analysis of Pruning Optimization Technology Based on Deep Learning

Abstract

Neural network models have become a major topic in many areas in recent years due to their excellent problem-solving ability. However, during the training process in the real application, neural network models usually have relatively high requirements on the performance of the platform, which limits their applications, especially in battery-driven devices or embedded platforms with relatively weak performance. Therefore, the importance of optimizing and pruning neural network models is very important. This paper analyzes four categories of pruning techniques, including channel pruning, neuron pruning, weight pruning and layer pruning. This paper investigates its main ideas and particular pruning methods and compares the optimised effects of these pruning techniques in different neural network models through experiments. This paper discusses neuron pruning on the VGG-16 model, weight pruning on ResNet and AlexNet, and layer pruning on the YOLOV3 model, respectively. The parameter amount and inference speed before and after pruning are compared, and the accuracies drop before and after pruning are verified on data sets such as CIFAR-10.

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