SPGNet: Serial and Parallel Group Network

Abstract

Neural-network Processing Units (NPU), which specializes in the acceleration of deep neural networks (DNN), is of great significance to latency-sensitive areas like robotics or edge computing. However, there are few works focusing on the network design for NPU in recent studies. Most of the popular lightweight structures (e.g. MobileNet) are designed with depthwise convolution, which has less computation in theory but is not friendly to existing hardwares, and the speed tested on NPU is not always satisfactory. Even under similar FLOPs (the number of multiply-accumulates), vanilla convolution operation is always faster than depthwise one. In this paper, we will propose a novel architecture named Serial and Parallel Group Network (SPGNet), which can capture discriminative multi-scale information and at the same time keep the structure compact. Extensive evaluations have been conducted on different computer vision tasks, e.g. image classification (CIFAR and ImageNet), object detection (PASCAL VOC and MS COCO) and person re-identification (Market-1501 and DukeMTMC-ReID). The experimental results show that our proposed SPGNet can achieve comparable performance with the state-of-the-art networks while the speed is 120% faster than MobileNetV2 under similar FLOPS and over 300% faster than GhostNet with similar accuracy on NPU.