Performance Analysis of Deep Neural Network Based on Channel Pruning

Model compression technology, a crucial aspect of neural network models, offers a range of benefits. It reduces the number of parameters and computational load, thereby shrinking the model size, enhancing inference speed, decreasing memory usage, and saving power. This article delves into the research of model compression technology for neural network models, focusing on channel pruning algorithms and model compression methods based on the Batch Normalization (BN) layer. The goal is to reduce the number of model parameters and computational load, leading to a smaller model size, faster inference speed, reduced memory usage, and saved power. The article applies sparse regularization to the scaling factors of the BN layer, serving as the basis for determining channel importance and reducing model complexity. It then presents experimental comparisons on VGGNet-16, ResNet-164, and DenseNet-40 neural network models, including standard training, sparse regularization, and pruning fine-tuning training results. This underscores the crucial role of research and advancements in model compression technology in achieving optimized neural network models that strike a balance between efficiency and performance.