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Energy-Aware HEVC Software Decoding On Mobile Heterogeneous Multi-Cores Architectures

Authors Mohammed Bey Ahmed Khernache , Jalil Boukhobza , Yahia Benmoussa, Daniel Menard

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Author Details

Mohammed Bey Ahmed Khernache
  • Univ. Bretagne-Sud, UMR 6285, Lab-STICC, France
Jalil Boukhobza
  • Lab-STICC UMR CNRS 6285, ENSTA Bretagne, France
Yahia Benmoussa
  • Univ. M’hamed Bougara, LMSS, Algeria
Daniel Menard
  • INSA de Rennes, UMR CNRS 6164 IETR Image Group, France

Acknowledgements

This work was supported by BPI France, Cap Digital, and Région Ile de France through the French project EFIGI.

Cite AsGet BibTex

Mohammed Bey Ahmed Khernache, Jalil Boukhobza, Yahia Benmoussa, and Daniel Menard. Energy-Aware HEVC Software Decoding On Mobile Heterogeneous Multi-Cores Architectures. In 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). Open Access Series in Informatics (OASIcs), Volume 100, pp. 4:1-4:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/OASIcs.PARMA-DITAM.2022.4

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.

Subject Classification

ACM Subject Classification
  • Hardware → Platform power issues
  • Hardware → Chip-level power issues
  • Computing methodologies → Classification and regression trees
  • Computer systems organization → Multicore architectures
Keywords
  • energy consumption
  • mobile platform
  • heterogeneous architecture
  • software video decoding
  • hardware video decoding
  • HEVC

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