Leakage-Aware Predictive Thermal Management for Multicore Systems Using Echo State Network

Abstract

Leakage power is becoming significant in new generation IC chips. As leakage power is nonlinearly related to temperature, it is challenging to manage the thermal behavior of today’s multicore systems, since thermal management becomes a nonlinear control problem. In this paper, a new predictive dynamic thermal management (DTM) method with neural network thermal model is proposed to naturally consider the inherent nonlinearity between leakage and temperature. We start with analyzing the problems of using recurrent neural network (RNN) to build the nonlinear thermal model, and point out that there is exploding gradient induced long-term dependencies problem, leading to large model prediction errors. Based on this analysis, we further propose to use echo state network (ESN), which is a special type of RNN, as the leakage-aware nonlinear thermal model. We theoretically and experimentally show that ESN achieves much higher accuracy by completely avoiding the long-term dependencies problem. On top of this nonlinear ESN thermal model, we propose a novel model predictive control (MPC) scheme called ESN MPC, which uses iterative steps to find the optimal future power recommendations for thermal management. Being able to consider the leakage-temperature nonlinear effects and equipped with advanced control technique, the new method achieves an overall high quality temperature management with smooth and accurate temperature tracking. The experimental results show the new method outperforms the state-of-the-art leakage-aware multicore DTM method in both temperature management quality and computing overhead.