19167

AI Benchmark: All About Deep Learning on Smartphones in 2019

Andrey Ignatov, Radu Timofte, Andrei Kulik, Seungsoo Yang, Ke Wang, Felix Baum, Max Wu, Lirong Xu, Luc Van Gool
ETH Zurich
arXiv:1910.06663 [cs.PF], (15 Oct 2019)

@misc{ignatov2019ai,

   title={AI Benchmark: All About Deep Learning on Smartphones in 2019},

   author={Andrey Ignatov and Radu Timofte and Andrei Kulik and Seungsoo Yang and Ke Wang and Felix Baum and Max Wu and Lirong Xu and Luc Van Gool},

   year={2019},

   eprint={1910.06663},

   archivePrefix={arXiv},

   primaryClass={cs.PF}

}

Download Download (PDF)   View View   Source Source   

495

views

The performance of mobile AI accelerators has been evolving rapidly in the past two years, nearly doubling with each new generation of SoCs. The current 4th generation of mobile NPUs is already approaching the results of CUDA-compatible Nvidia graphics cards presented not long ago, which together with the increased capabilities of mobile deep learning frameworks makes it possible to run complex and deep AI models on mobile devices. In this paper, we evaluate the performance and compare the results of all chipsets from Qualcomm, HiSilicon, Samsung, MediaTek and Unisoc that are providing hardware acceleration for AI inference. We also discuss the recent changes in the Android ML pipeline and provide an overview of the deployment of deep learning models on mobile devices. All numerical results provided in this paper can be found and are regularly updated on the official project website.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2019 hgpu.org

All rights belong to the respective authors

Contact us: