An Analytical Dynamic Scaling of Supply Voltage and Body Bias Based on Parallelism-Aware Workload and Runtime Distribution

Abstract

Dynamic voltage and frequency scaling (DVFS) for a parallel software program is crucial for lowering the ever-increasing power consumption of multiprocessor systems-on-chips (SoCs). In this paper, we propose an analytical DVFS method that judiciously exploits slack by considering the varying parallelism over each path in a task graph. The proposed method overcomes the conventional pessimistic assumption on the remaining workload, i.e., worst-case execution cycle. It yields minimum average energy consumption by utilizing the runtime distribution of a software program while satisfying the deadline constraints. The proposed method tackles leakage power consumption as well as dynamic power consumption by combined V-dd/V-bb scaling. Compared to conventional method [15], experimental results show that the proposed method provides up to 49.20% energy reduction for a set of synthetic task graphs and yields 23.93% and 27.15% energy reductions for two multimedia applications, namely, the H.264 encoder and decoder, respectively.