Green metadata based adaptive DVFS for energy efficient video decoding

Yahia Benmoussa 1 Eric Senn 2 Nicolas Derouineau Nicolas Tizon Jalil Boukhobza 1
1 Lab-STICC_UBO_CACS_MOCS
UBO - Université de Brest, Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
2 Lab-STICC_UBS_CACS_MOCS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : We present in this paper GM-DVFS, an adaptive DVFS scheme for energy efficient decoding of H.264 videos. GM-DVFS uses metadata (normalized by the MPEG Green Metadata standard) providing information about the upcoming workload. In the solution we propose, these metadata are processed within an adaptive filter to build dynamically an accurate video decoding complexity model. This model serves to calculate the minimal required processor frequency for decoding a video frame while guaranteeing the real time constraints. Our performance evaluations showed that the proposed algorithm is able to converge to an accurate complexity model (4%) in less than 1 second (in the worst case). Moreover, it is simple to implement (250 lines of C code) and induces very low overhead (1400 cycles/frames). On the other hand, it allows to achieve up to 46% energy saving as compared to the on demand Linux DVFS governor.
Type de document :
Communication dans un congrès
26th International Workshop on Power and Timing Modeling, Optimization and Simulation (PATMOS),, Sep 2016, Bremen, Germany. 2016, 〈10.1109/PATMOS.2016.7833693〉
Liste complète des métadonnées

http://hal.univ-brest.fr/hal-01557982
Contributeur : Jalil Boukhobza <>
Soumis le : jeudi 6 juillet 2017 - 17:58:42
Dernière modification le : mardi 16 janvier 2018 - 15:54:24

Identifiants

Citation

Yahia Benmoussa, Eric Senn, Nicolas Derouineau, Nicolas Tizon, Jalil Boukhobza. Green metadata based adaptive DVFS for energy efficient video decoding. 26th International Workshop on Power and Timing Modeling, Optimization and Simulation (PATMOS),, Sep 2016, Bremen, Germany. 2016, 〈10.1109/PATMOS.2016.7833693〉. 〈hal-01557982〉

Partager

Métriques

Consultations de la notice

66