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Energy Consumption of Video Decoding
Abstract: In this paper, we aim to bridge the gap between the energy efficiency of software and hardware video decoders by combining both DVFS and parallelism. For this purpose, we... View more
In this paper, we aim to bridge the gap between the energy efficiency of software and hardware video decoders by combining both DVFS and parallelism. For this purpose, we, first, propose an adaptive DVFS algorithm for energy efficient mono-core decoding of H.264 videos. The proposed solution uses metadata (normalized by MPEG) providing information about the upcoming workload. These metadata are processed within an adaptive filter to build dynamically an accurate complexity model used to calculate the minimal processor frequencies for decoding video frames while guaranteeing real time constraints. Then, we generalize the proposed DVFS to slice-based multi-threaded parallel video decoders on multi-core platforms. Our performance evaluations showed that the proposed algorithm for mono-core decoding is able to converge to an accurate complexity model (4 percent) in less than 1 second. Moreover, it is simple to implement, induces very low overhead and achieves up to 46 percent energy saving as compared to the ondemand Linux DVFS governor. On the other hand, joint use of parallelism and DVFS allows 720p software video decoding with only 17 percent more energy consumption as compared to a hardware video decoder.
Date of Publication: 04 December 2017
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The new trends in video application usage (Youtube, IPTV, Video conferencing) combined with the market explosion of
multimedia consumer electronics rise new challenges for mobile device manufacturers. Indeed, huge usage of processing
resources during video decoding induces a high power consumption [1]. This
leads to a drastic decrease in mobile devices autonomy as battery technologies are not evolving fast enough to absorb
the ever-growing energy requirements [2].
2009 IEEE International Conference on Multimedia and Expo
2007 IEEE 9th Workshop on Multimedia Signal Processing
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In this paper, we aim to bridge the gap between the energy efficiency of software and hardware video decoders by combining both DVFS and parallelism. For this purpose, we, first, propose an adaptive DVFS algorithm for energy efficient mono-core decoding of H.264 videos. The proposed solution uses metadata (normalized by MPEG) providing information about the upcoming workload. These metadata ...
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In this paper, we aim to bridge the gap between the energy efficiency of software and hardware video decoders by combining both DVFS and parallelism. For this purpose, we, first, propose an adaptive DVFS algorithm for energy efficient mono-core decoding of H.264 videos. The proposed solution uses metadata (normalized by MPEG) providing information about the upcoming workload. These metadata ...
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In this paper, we aim to bridge the gap between the energy efficiency of software and hardware video decoders by combining both DVFS and parallelism. For this purpose, we, first, propose an adaptive DVFS algorithm for energy efficient mono-core decoding of H.264 videos. The proposed solution uses metadata (normalized by MPEG) providing information about the upcoming workload. These metadata ...
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