Analyzing the Energy-Efficiency of Vision Kernels on Embedded CPU, GPU and FPGA Platforms
2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)(2019)
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.
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Key words
Kernel,Field programmable gate arrays,Graphics processing units,Benchmark testing,Hardware,Complexity theory,Optical filters
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