Phase-driven Learning-based Dynamic Reliability Management For Multi-core Processors

In this paper, we propose a phase-driven Q-learning based dynamic reliability management (DRM) technique for multi-core processors to solve DRM problems of maximizing the processor performance subject to a large class of reliability constraints by turning ON/OFF cores and dynamic voltage frequency scaling. Our technique utilizes the existing methods to detect program phases (i.e. [17]) and learns (rather than obtaining at the off-line stage) the optimal configuration of the multi-core processor for each phase. Our technique outperforms the existing learning-based DRM methods in managing programs with highly diverse phases. Our proposed technique is evaluated by solving a DRM problem in 3D CPUs of maximizing processor performance subject to the electromigration induced power delivery network reliability constraint. Compared to the latest Q-learning based DRM technique [11], our method can achieve more than 1.3× improvement in performance with 77% memory savings.