Latest revision as of 17:28, 28 January 2021

Abstract

Developing high performance embedded vision applications requires balancing run-time performance with energy constraints. Given the mix of hardware accelerators that exist for embedded computer vision (e.g. multi-core CPUs, GPUs, and FPGAs), and their associated vendor optimized vision libraries, it becomes a challenge for developers to navigate this fragmented solution space. To aid with determining which embedded platform is most suitable for their application, we conduct a comprehensive benchmark of the run-time performance and energy efficiency of a wide range of vision kernels. We discuss rationales for why a given underlying hardware architecture innately performs well or poorly based on the characteristics of a range of vision kernel categories. Specifically, our study is performed for three commonly used HW 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 the others for simple kernels. While for more complicated kernels and complete vision pipelines, 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 application's pipeline complexity grows.

Comment: 8 pages, Design Automation Conference (DAC), The 15th IEEE International Conference on Embedded Software and Systems, 2019

Original document

The different versions of the original document can be found in:

• http://arxiv.org/abs/1906.11879

• http://arxiv.org/pdf/1906.11879

• http://xplorestaging.ieee.org/ielx7/8771047/8782437/08782524.pdf?arnumber=8782524,

http://dx.doi.org/10.1109/icess.2019.8782524

• https://dblp.uni-trier.de/db/journals/corr/corr1906.html#abs-1906-11879,

https://lib.dr.iastate.edu/ece_pubs/218,

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1219&context=ece_pubs,

https://hgpu.org/?p=18909,

https://arxiv.org/abs/1906.11879,

https://arxiv.org/pdf/1906.11879,

https://academic.microsoft.com/#/detail/2965571038