
@conference {QasDen19A,
	title = {Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels},
	booktitle = {Proceedings of the IEEE International Conference on Embedded Software and Systems (ICESS)},
	year = {2019},
	month = {June},
	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{\textendash}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{\textendash}22.3X. It is also observed that the FPGA performs increasingly better as a vision application{\textquoteright}s pipeline complexity grows.},
	author = {Murad Qasaimeh and Kristof Denolf and Jack Lo and Kees Vissers and Joseph Zambreno and Phillip Jones}
}
