SNPF: Sensitiveness-Based Network Pruning Framework for Efficient Edge Computing

Yiheng Lu, Ziyu Guan, Wei Zhao, Maoguo Gong, Wenjing Wang, Kai Sheng

Abstract: Convolutional neural networks (CNNs) are used comprehensively in the field of the Internet of Things (IoTs), such as mobile phones, surveillance, and satellite. However, the deployment of CNNs is difficult because the structure of hand-designed networks is complicated. Therefore, we propose a sensitiveness-based network pruning framework (SNPF) to reduce the size of original networks to save computation resources. SNPF will evaluate the importance of each convolutional layer by the reconstruction of inference accuracy when we add extra noise to the original model, and then remove filters in terms of the degree of sensitiveness for each layer. Compared with previous weight-norm-based pruning methods, such as “ $\mathscr {C}_{1}$ -norm,” “BatchNorm-Pruning,” and “Taylor-Pruning,” SNPF is robust to the update of parameters, which can avoid the inconsistency of evaluation for filters if the parameters of the pretrained model are not fully optimized. Namely, SNPF, can prune the network at the early training stage to save computation resources. We test our method on three prevalent models of VGG-16, ResNet-18, ResNet-50 and a customized Conv-4 with 4 convolutional layers. They are then tested on CIFAR-10, CIFAR-100, ImageNet, and MNIST, respectively. Impressively, we observe that even when the VGG-16 is only trained with 50 epochs, we can get the same evaluation of layer importance as the results when the model is fully trained. Additionally, we can also achieve comparable pruning results to previous weight-oriented methods on the other three models.