DcaNAS: efficient convolutional network Design for Desktop CPU platforms

Abstract

The hardware platform is a significant consideration in efficient CNN model design. Most lightweight networks are based on GPUs and mobile devices. However, they are usually not efficient nor fast enough for desktop CPU platforms. In this paper, we aim to explore the design of highly-efficient convolutional architectures for desktop CPU platforms. To achieve our goal, we first derive a series of CNN model design guidelines for CPU-based devices by comparing different computing platforms. Based on these proposed guidelines, we further present a Desktop CPU-Aware network architecture search (DcaNAS) to search for the optimal network structure with lower CPU latency. By combining automatic search and manual design, our DcaNAS achieves better flexibility and efficiency. On the ImageNet benchmark, we employ DcaNAS to produce two CPU-based lightweight CNN models: DcaNAS-L for higher accuracy and DcaNAS-S for faster speed. On a single CPU core, DcaNAS-L achieves 78.8% Top-1 (94.6% Top-5) accuracy at 13.6 FPS (73.5 ms), and our DcaNAS-S achieves extremely low CPU latency (43.1 ms). The results show that our DcaNAS method can obtain new state-of-the-art CPU-based networks.