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Benchmarking Modern Edge Devices for AI Applications

Pilsung Kang, Jongmin Jo
Division of Computer Science and Engineering, Sun Moon University, Asan, South Korea
IEICE Transactions on Information and Systems, E104-D, No.3, 2021

@article{kang2021benchmarking,

   title={Benchmarking Modern Edge Devices for AI Applications},

   author={KANG, Pilsung and JO, Jongmin},

   journal={IEICE Transactions on Information and Systems},

   volume={104},

   number={3},

   pages={394–403},

   year={2021},

   publisher={The Institute of Electronics, Information and Communication Engineers}

}

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AI (artificial intelligence) has grown at an overwhelming speed for the last decade, to the extent that it has become one of the mainstream tools that drive the advancements in science and technology. Meanwhile, the paradigm of edge computing has emerged as one of the foremost areas in which applications using the AI technology are being most actively researched, due to its potential benefits and impact on today’s widespread networked computing environments. In this paper, we evaluate two major entry-level offerings in the state-of-the-art edge device technology, which highlight increased computing power and specialized hardware support for AI applications. We perform a set of deep learning benchmarks on the devices to measure their performance. By comparing the performance with other GPU (graphics processing unit) accelerated systems in different platforms, we assess the computational capability of the modern edge devices featuring a significant amount of hardware parallelism.
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