Efficient Peak Power Estimation Using Probabilistic Cost-Benefit Analysis

Abstract

Estimation of peak power consumption is an essential task in order to design reliable systems. Optimistic design choices can make the circuit unreliable and vulnerable to power attacks, whereas pessimistic design can lead to unacceptable design overhead. The power virus problem is defined as finding input patterns that can maximize switching activity (dynamic power dissipation) in digital circuits. In this paper, we present a fast and simple to implement power virus generation technique utilizing a probabilistic cost-benefit analysis. To maximize switching activity, our proposed algorithm iteratively enables transitions in high fan-out gates while considering the trade-off between switching of new gates (benefit) and blocking of gate transitions in the future iterations (cost) due to switching of the currently selected one. Extensive experiments using both combinational and sequential benchmarks demonstrate that our approach can achieve up to 64% more toggles (30.7% on average) for zero-delay model and improvements of up to 319% (109% on average) for unit-delay model compared to the state-of-the-art techniques.