Resilient Dynamic Power Management under Uncertainty

With the increasing levels of variability and randomness in the characteristics and behavior of manufactured nanoscale structures and devices, achieving performance optimization under process, voltage, and temperature (PVT) variations as well as current, voltage, and thermal (CVT) stress has become a daunting, yet vital, task. In this paper, we present a stochastic dynamic power management (DPM) framework to improve the accuracy of decision making under probabilistic conditions induced by PVT variations and/or stress. More precisely, we propose a resilient power management technique that guarantees to select an optimal policy under sources of uncertainty. A key characteristic of the proposed technique is that the effects of uncertainties due to variability and stress are captured by stochastic processes which control a self- improving power manager. Simulation results with a 65 nm processor design show that, compared to the worst-case PVT conditions, the proposed DPM technique ensures energy efficiency, while reducing the uncertain behaviors of the system.