基于FPGA的深度学习目标检测系统的设计与实现

Design and implementation of FPGA-based deep learning object detection system

针对当前深度学习目标检测算法计算复杂度高和内存需求大等问题,设计并实现了一种基于FPGA的深度学习目标检测系统。设计对应YOLOv2-Tiny目标检测算法的硬件加速器,对加速器各模块的处理时延建模,给出卷积计算模块的详细设计。实验结果表明,与CPU相比,CPU+FPGA的异构系统是双核ARM-A9能效的67.5倍,Xeon的94.6倍;速度是双核ARM-A9的84.4倍,Xeon的5.5倍左右。并且,当前设计在性能上超过之前的工作。

Aiming at the problems of higher computational complexity and larger memory requirements of current object detection algorithm, we designed and implemented an FPGA-based deep learning object detection system. We also designed the hardware accelerator corresponding to the YOLOv2-Tiny object detection algorithm, modeled the processing delay of each accelerator module, and describe the design of the convolution module. The experimental results show that it is 5.5x and 94.6x of performance and energy gains respectively when comparing with the software Darknet on an 8-core Xeon server, and 84.8x and 67.5x over the software version on the dual-core ARM cortex-A9 on Zynq. Also, the current design outperforms the previous work in performance.