Analyzing the Energy-Efficiency of Vision Kernels on Embedded CPU, GPU and FPGA Platforms

Abstract

This paper presents a benchmark of the energy efficiency of a wide range of vision kernels on three commonly used hardware accelerators for embedded vision applications: ARM57 CPU, Jetson TX2 GPU and ZCU102 FPGA, using their vendor optimized vision libraries: OpenCV, VisionWorks and xfOpenCV. Our results show that the GPU achieves an energy/frame reduction ratio of 1.1-3.2x compared to CPU and FPGA for simple kernels. While for more complicated kernels, the FPGA outperforms the others with energy/frame reduction ratios of 1.2-22.3x. It is also observed that the FPGA performs increasingly better as a vision kernel's complexity grows.