Parallel Implementation of CNN on Multi-FPGA Cluster

Abstract

We developed a PYNQ cluster that consists of economical Zynq boards, called M-KUBOS, that are interconnected through low-cost high-performance GTH serial links. For the software environment, we employed the PYNQ open-source software platform. The PYNQ cluster is anticipated to be a multi-access edge computing (MEC) server for 5G mobile networks. We implemented the ResNet-50 inference accelerator on the PYNQ cluster for image recognition of MEC applications. By estimating the execution time of each ResNet-50 layer, layers of ResNet-50 were divided into multiple boards so that the execution time of each board would be as equal as possible for efficient pipeline processing. Owing to the PYNQ cluster in which FPGAs were directly connected by high-speed serial links, stream processing without network bottlenecks and pipeline processing between boards were readily realized. The implementation on 4 boards achieved 292 GOPS performance, 75.1 FPS throughput, and 7.81 GOPS/W power efficiency. It achieved 17 times faster speed and 130 times more power efficiency compared to the implementation on the CPU, and 5.8 times more power efficiency compared to the implementation on the GPU.