Reinforcement Learning-Based Power Management Policy for Mobile Device Systems

This paper presents a power management policy that utilizes reinforcement learning to increase the power efficiency of mobile device systems based on a multiprocessor system-on-a-chip (MPSoC). The proposed policy predicts a system's characteristics and learns power management controls to adapt to the variations in the system. We consider the behavioral characteristics of systems that run on mobile devices under diverse scenarios. Therefore, the policy can flexibly manage the system power regardless of the application scenario and achieve lower energy consumption without compromising the user satisfaction. The average energy per unit quality of service (QoS) of the proposed policy is lower than that of the previous six dynamic voltage/frequency scaling governors by 31.66%. Furthermore, we reduce the runtime overhead by implementing the proposed policy as hardware. We implemented the policy on the field programmable gate array (FPGA) and construct a communication interface between the central processing units (CPUs) and the hardware of the proposed policy. Decision-making by the hardware-implemented policy is 3.92 times faster than by the software-implemented policy.