Controlling Uncertainty and Handling Variability in System-Level Dynamic Power Management

Improving the Efficiency of Power Management Techniques by Using Bayesian Classification In an ISQED-08 paper, we presented a supervised learning based dynamic power management (DPM) framework for a multicore processor, where a power manager (PM) learns to predict the system performance state from some readily available input features (such as the state of service queue occupancy and the task arrival rate) and then uses this predicted state to look up the optimal power management action from a pre-computed policy lookup table. The motivation for utilizing supervised learning in the form of a Bayesian classifier is to reduce overhead of the PM which has to recurrently determine and issue voltage-frequency setting commands to each processor core in the system. Experimental results reveal that the proposed Bayesian classification based DPM technique ensures system-wide energy savings under rapidly and widely varying workloads.

Resilient Dynamic Power Management under Uncertainty In a DATE-08 paper, we presented a stochastic framework to improve the accuracy of decision making during dynamic power management, while considering manufacturing process and/or design induced uncertainties. More precisely, the uncertainties are captured by a partially observable semi-Markov decision process and the policy optimization problem is formulated as a mathematical program based on this model. Experimental results with a RISC processor in 65nm technology demonstrate the effectiveness of the technique and show that the proposed uncertainty-aware power management technique ensures system-wide energy savings under statistical circuit parameter variations.