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Erschienen in:
Multi–scale Recursive and Perception–Distortion Controllable Image Super–Resolution Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

AI Benchmark: Running Deep Neural Networks on Android Smartphones

verfasst von : Andrey Ignatov, Radu Timofte, William Chou, Ke Wang, Max Wu, Tim Hartley, Luc Van Gool

Erschienen in: Computer Vision – ECCV 2018 Workshops

Verlag: Springer International Publishing

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Abstract

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 (http://​ai-benchmark.​com) that are covering all main existing hardware configurations.

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Vorheriges Kapitel PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study
Nächstes Kapitel PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
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Metadaten
Titel
AI Benchmark: Running Deep Neural Networks on Android Smartphones
verfasst von
Andrey Ignatov
Radu Timofte
William Chou
Ke Wang
Max Wu
Tim Hartley
Luc Van Gool
Copyright-Jahr
2019
Buch
Computer Vision – ECCV 2018 Workshops

Print ISBN: 978-3-030-11020-8

Electronic ISBN: 978-3-030-11021-5

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-11021-5_19

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