License: 
Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.PARMA-DITAM.2022.4
URN: urn:nbn:de:0030-drops-161206
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2022/16120/
Bey Ahmed Khernache, Mohammed ;
Boukhobza, Jalil ;
Benmoussa, Yahia ;
Menard, Daniel
Energy-Aware HEVC Software Decoding On Mobile Heterogeneous Multi-Cores Architectures
Abstract
Video content is becoming increasingly omnipresent on mobile platforms thanks to advances in mobile heterogeneous architectures. These platforms typically include limited rechargeable batteries which do not improve as fast as video content. Most state-of-the-art studies proposed solutions based on parallelism to exploit the GPP heterogeneity and DVFS to scale up/down the GPP frequency based on the video workload. However, some studies assume to have information about the workload before to start decoding. Others do not exploit the asymmetry character of recent mobile architectures. To address these two challenges, we propose a solution based on classification and frequency scaling. First, a model to classify frames based on their type and size is built during design-time. Second, this model is applied for each frame to decide which GPP cores will decode it. Third, the frequency of the chosen GPP cores is dynamically adjusted based on the output buffer size. Experiments on real-world mobile platforms show that the proposed solution can save more than 20% of energy (mJ/Frame) compared to the Ondemand Linux governor with less than 5% of miss-rate. Moreover, it needs less than one second of decoding to enter the stable state and the overhead represents less than 1% of the frame decoding time.
BibTeX - Entry
@InProceedings{beyahmedkhernache_et_al:OASIcs.PARMA-DITAM.2022.4,
author = {Bey Ahmed Khernache, Mohammed and Boukhobza, Jalil and Benmoussa, Yahia and Menard, Daniel},
title = {{Energy-Aware HEVC Software Decoding On Mobile Heterogeneous Multi-Cores Architectures}},
booktitle = {13th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 11th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2022)},
pages = {4:1--4:13},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-231-0},
ISSN = {2190-6807},
year = {2022},
volume = {100},
editor = {Palumbo, Francesca and Bispo, Jo\~{a}o and Cherubin, Stefano},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2022/16120},
URN = {urn:nbn:de:0030-drops-161206},
doi = {10.4230/OASIcs.PARMA-DITAM.2022.4},
annote = {Keywords: energy consumption, mobile platform, heterogeneous architecture, software video decoding, hardware video decoding, HEVC}
}
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Keywords: |
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energy consumption, mobile platform, heterogeneous architecture, software video decoding, hardware video decoding, HEVC |
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Collection: |
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13th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 11th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2022) |
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Issue Date: |
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2022 |
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Date of publication: |
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08.06.2022 |
