AI Benchmark: Running Deep Neural Networks on Android Smartphones
ETH Zurich
arXiv:1810.01109 [cs.AI], (15 Oct 2018)
@article{ignatov2018benchmark,
title={AI Benchmark: Running Deep Neural Networks on Android Smartphones},
author={Ignatov, Andrey and Timofte, Radu and Chou, William and Wang, Ke and Wu, Max and Hartley, Tim and Gool, Luc Van},
year={2018},
month={oct},
archivePrefix={"arXiv"},
primaryClass={cs.AI}
}
Over the last years, the computational power of mobile devices such as smartphones and tablets has grown dramatically, reaching the level of desktop computers available not long ago. While standard smartphone apps are no longer a problem for them, there is still a group of tasks that can easily challenge even high-end devices, namely running artificial intelligence algorithms. In this paper, we present a study of the current state of deep learning in the Android ecosystem and describe available frameworks, programming models and the limitations of running AI on smartphones. We give an overview of the hardware acceleration resources available on four main mobile chipset platforms: Qualcomm, HiSilicon, MediaTek and Samsung. Additionally, we present the real-world performance results of different mobile SoCs collected with AI Benchmark that are covering all main existing hardware configurations.
October 21, 2018 by hgpu
