A methodology for performance/energy consumption characterization and modeling of video decoding on heterogeneous SoC and its applications

To meet the increasing complexity of mobile multimedia applications, SoCs equipping modern mobile devices integrate powerful heterogeneous processing elements among which Digital Signal Processors (DSP) and General Purpose Processors (GPP) are the most common ones. Due to the ever-growing gap between battery lifetime and hardware/software complexity in addition to application's computing power needs, the energy saving issue becomes crucial in the design of such architectures. In this context, we propose in this paper an end-to-end study of video decoding on both GPP and DSP. The study was achieved thanks to a two steps methodology: (1) a comprehensive characterization and evaluation of the performance and the energy consumption of video decoding, (2) an accurate high level energy model is extracted based on the characterization step. The characterization of the video decoding is based on an experimental methodology and was achieved on an embedded platform containing a GPP and a DSP. This step highlighted the importance of considering the end-to-end decoding flow when evaluating the energy efficiency of video decoding application. The measurements obtained in this step were used to build a comprehensive analytical energy model for video decoding on both GPP and DSP. Thanks to a sub-model decomposition, the developed model estimates the energy consumption in terms of processor clock frequency and video bit-rate in addition to a set of constant coefficients which are related to the video complexity, the operating system and the considered hardware architecture. The obtained model gave very accurate results (R-2 = 97%) for both GPP and DSP energy consumption. Finally, based on the results emerged from the modeling methodology, we show how one can build rapidly a video decoding energy model for a given target architecture without executing the full characterization steps described in this paper. (C) 2014 Elsevier B.V. All rights reserved.