Structured Pruning for Deep Convolutional Neural Networks via Adaptive Sparsity Regularization

Tuanjie Shao, Dongkun Shin

Abstract: Structured pruning is a promising method to reduce the computational cost and memory load, and then accelerate the inference process of deep neural networks. Therefore, it facilitates the deep convolutional model application on resource-constrained devices, such as IoT devices and embedded systems. To make a compact deep convolutional neural network model, many network pruning methods enforce the sparsity by imposing sparse constraints on weight parameters during training and pruning some insignificant weights. However, those methods usually impose sparse constraints without any guidance, while simply limiting the compression rate. This paper proposes a dynamic scheme to impose the sparse constraints according to the filter weights, which can guide sparsity-induced training to choose import channels in a deep convolutional neural network. The extensive experiments present that our adaptive sparsity-induced training is more efficient than a static training scheme. Compared to the existing techniques, the proposed method achieved a superior pruning performance on CIFAR10 with 91.5% parameters reduction and 61.6% floating point operations (FLOPs). On the object detection task, our method achieved 91.2% parameter reductions and 65.8% FLOPs reductions on YOLOV5s. Furthermore, we observed a significant acceleration on the inference process of the pruned models on real resource-constrained devices.