Co-manage power delivery and consumption for manycore systems using reinforcement learning

Maintaining high energy efficiency has become a critical design issue for high-performance systems. Many power management techniques have been proposed for the processor cores such as dynamic voltage and frequency scaling (DVFS). However, very few solutions consider the power losses suffered on the power delivery system (PDS), despite the fact that they have a significant impact on the system overall energy efficiency. With the explosive growth of system complexity and highly dynamic workloads variations, it is challenging to find the optimal power management policies which can effectively match the power delivery with the power consumption. To tackle the above problems, we propose a reinforcement learning-based power management scheme for manycore systems to jointly monitor and adjust both the PDS and the processor cores aiming to improve system overall energy efficiency. The learning agents distributed across power domains not only manage the power states of processor cores but also control the on/off states of on-chip VRs to proactively adapt to the workload variations. Experimental results with realistic applications show that when the proposed approach is applied to a large-scale system with a hybrid PDS, it lowers the system overall energy-delay-product (EDP) by 41% than a traditional monolithic DVFS approach with a bulky off-chip VR.