Compressing CNNs Using Multilevel Filter Pruning for the Edge Nodes of Multimedia Internet of Things
Xingang Liu, Lishuai Wu, Cheng Dai, Han‐Chieh Chao
Abstract
Multimedia Internet-of-Things (IoT) systems have been widely utilized in various computer vision tasks and significantly integrated computer vision and networking capabilities. In these systems, convolutional neural networks (CNNs) perform a preliminary analysis of the collected video or image information in the edge devices. However, the high computational cost and huge storage consumption of the complex CNNs prevent their deployment on mobile-edge devices that have limited computational resource and memory. In this article, we aim to simultaneously accelerate and compress CNNs via a multilevel filter pruning (MFP) algorithm, to alleviate the dependence on the hardware of IoT edge nodes. First, a global pruning sensitivity order is defined, which could guide us to perform preliminary pruning from the perspective of convolutional layers' sensitivity. Then, the functional index of each filter is judged by the image entropy of its output feature map, which contributes to further pruning from the perspective of filter function importance. Finally, the moderate fine tuning is adopted to recover the network capability. The experimental results show that the proposed MFP algorithm could reduce 54.5% floating-point operations and 31.9% graphics memory for VGG-16 on CIFAR-10, and achieve 5.45 × floating-point acceleration and 19.70 × storage reduction for VGG-16 on ImageNet. In the reconstruction phase, the algorithm could recover the network capability much faster than the existing pruning algorithms.