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 (R² = 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.     

The impact of topology on energy consumption for collection tree protocols: An experimental assessment through evolutionary computation

The analysis of worst-case behavior in wireless sensor networks is an extremely difficult task, due to the complex interactions that characterize the dynamics of these systems. In this paper, we present a new methodology for analyzing the performance of routing protocols used in such networks. The approach exploits a stochastic optimization technique, specifically an evolutionary algorithm, to generate a large, yet tractable, set of critical network topologies; such topologies are then used to infer general considerations on the behaviors under analysis. As a case study, we focused on the energy consumption of two well-known ad hoc routing protocols for sensor networks: the multi-hop link quality indicator and the collection tree protocol. The evolutionary algorithm started from a set of randomly generated topologies and iteratively enhanced them, maximizing a measure of “how interesting” such topologies are with respect to the analysis. In the second step, starting from the gathered evidence, we were able to define concrete, protocol-independent topological metrics which correlate well with protocols’ poor performances. Finally, we discovered a causal relation between the presence of cycles in a disconnected network, and abnormal network traffic. Such creative processes were made possible by the availability of a set of meaningful topology examples. Both the proposed methodology and the specific results presented here – that is, the new topological metrics and the causal explanation – can be fruitfully reused in different contexts, even beyond wireless sensor networks.