combined dynamic voltage scaling and adaptive body biasing for heterogeneous distributed real-time embedded systems
Dynamic voltage scaling (DVS) is a powerful technique for reducingdynamic power consumption in a computing system. However, astechnology feature size continues to scale, leakage power is increasing and willlimit power savings obtained by DVS alone. Previous system-level real-timescheduling approaches use DVS alone to optimize power consumption withoutconsidering leakage power. To overcome this limitation, we propose a newscheduling algorithm that combines DVS and adaptive body biasing (ABB)to simultaneously optimize both dynamic power consumption and leakagepower consumption for real-time distributed embedded systems. First, wederive an analytical expression to determine the optimal supply voltage andbody bias voltage under a given clock frequency. Based on this expression, wecompute the optimal energy consumption at a given clock frequency and analyzethe tradeoff between energy consumption and execution time for a set oftasks with precedence relationships and real-time constraints. We then proposea scheduling algorithm to reduce total power consumption under givenreal-time constraints. This algorithm also considers variations in power consumptionof different tasks and characteristics of different voltage-scalableprocessing elements (PEs) to maximize power reduction. Experimental resultsshow that the average power reduction of our technique with respect toDVS alone is 34.7\%, while the average saving compared to no voltage scalingis 68.3\% for the 0.07m technology.
dynamic voltage scaling dynamic scale adapt bias heterogeneity distributed real-time real-time embedded systems embedded systems systems power power consumption computation technologies feature size leakage power timing use optimality scheduling algorithm algorithm express clock energy consumption relationships constraint variation reduction experimentation time constraint power-save