基于ARM+FPGA异构平台的目标检测加速模块设计与实现

为解决基于深度学习目标检测模型规模大、在边缘设备上难以部署的问题, 以YOLO目标检测模型为例, 设计实现基于ARM+FPGA异构平台的目标检测加速模块。该系统使用剪枝、量化后的压缩模型, 在FPGA实现神经网络前向推理加速, 在ARM中实现加速器调度。实验结果表明, 部署至Xilinx ZCU102开发板上, 该模块在200 MHz工作频率下, 平均计算性能达到425.8 GOP/s, 推理压缩模型速度达到30.3 fps, 模块功耗为3.56 W, 证明该加速模块具备可配置性。

Design and Implementation of Object Detection Acceleration ModuleBased on an ARM+FPGA Heterogeneous Platform

Object detection algorithms based on deep learning use big models are difficult to be deployed at the edge. Taking YOLO (you only look once) object detection algorithm as an example, an acceleration module based on an ARM+FPGA heterogeneous platform is proposed. The FPGA chip accelerates the forward process of the compressed model while ARM is responsible for process scheduling. Experiment results show that the peak performance of the system reaches 425.8 GOP/s under 200 MHz working frequency. The system on a Xilinx ZCU102 board achieves a frame rate at 30.3 fps, while the power consumption is 3.56 W. It is also configurable.