An analytical dynamic scaling of supply voltage and body bias exploiting memory stall time variation

Abstract

Success of workload prediction, which is critical in achieving low energy consumption via dynamic voltage and frequency scaling (DVFS), depends on the accuracy of modeling the major sources of workload variation. Among them, memory stall time, whose variation is significant especially in case of memory-bound applications, has been mostly neglected or handled in too simplistic assumptions in previous works. In this paper, we present an analytical DVFS method which takes into account variations in both computation and memory stall cycles. The proposed method reduces leakage power consumption as well as switching power consumption through combined Î Î scaling. Experimental results on MPEG4 and H.264 decoder have shown that, compared to previous methods [3] and [6], our method achieves up to additional 30.0% and 15.8% energy reductions, respectively.