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Processor Load Analysis for Mobile Multimedia Streaming: The Implication of Power Reduction

机译:移动多媒体流的处理器负载分析:降低功耗的含义

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The software codec on mobile device introduces significant power consumption because the energy efficiency of general processor based system is much lower than that of the dedicated hardware such as ASIC based accelerator. Dynamical voltage scaling (DVS) is one of the most efficient techniques to promote the energy efficiency. Most existing papers on this topic use simple heuristics to predict processor load, and poor prediction accuracy is observed in experiments. We advocate intensive analysis on processor load before designing DVS framework and algorithm. Hence, we conduct load analysis on more than 600 processor load trace files for 57 test sequences and 98 representative clips from Internet. Basic statistical analysis and time series analysis are applied intensively to identify major characteristics of the processor load. The analysis shows that it is feasible to predict processor load using low order linear time series model if the load is sampled using feature period. Moreover, there is indeed significant potential to reduce the energy consumption. Based on the analysis results, we develop a fully adaptive DVS technique to adjust supply voltage online with controllable penalty
机译:由于基于通用处理器的系统的能效比基于ASIC的加速器之类的专用硬件的能效低得多,因此移动设备上的软件编解码器会引入大量功耗。动态电压缩放(DVS)是提高能源效率的最有效技术之一。关于此主题的大多数现有论文都使用简单的启发式方法来预测处理器负载,并且在实验中观察到较差的预测精度。在设计DVS框架和算法之前,我们主张对处理器负载进行深入分析。因此,我们对600多个处理器的负载跟踪文件进行了负载分析,以查找来自Internet的57个测试序列和98个代表性片段。基础统计分析和时间序列分析被广泛应用于确定处理器负载的主要特征。分析表明,如果使用特征周期对负载进行采样,则使用低阶线性时间序列模型预测处理器负载是可行的。而且,确实存在降低能耗的巨大潜力。根据分析结果,我们开发了一种完全自适应的DVS技术,可在线调节电源电压并具有可控的损失

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