Q-scheduler: A temperature and energy-aware deep Q-learning technique to schedule tasks in real-time multiprocessor embedded systems

Reducing energy consumption under processors' temperature constraints has recently become a pressing issue in real-time multiprocessor systems on chips (MPSoCs). The high temperature of processors affects the power and reliability of the MPSoC. Low energy consumption is necessary for real-time embedded systems, as most of them are portable devices. Efficient task mapping on processors has a significant impact on reducing energy consumption and the thermal profile of processors. Several state-of-the-art techniques have recently been proposed for this issue. This paper proposes Q-scheduler, a novel technique based on the deep Q-learning technology, to dispatch tasks between processors in a real-time MPSoC. Thousands of simulated tasks train Q-scheduler offline to reduce the system's power consumption under temperature constraints of processors. The trained Q-scheduler dispatches real tasks in a real-time MPSoC online while also being trained regularly online. Q-scheduler dispatches multiple tasks in the system simultaneously with a single process; the effectiveness of this ability is significant, especially in a harmonic real-time system. Experimental results illustrate that Q-scheduler reduces energy consumption and temperature of processors on average by 15% and 10%, respectively, compared to previous state-of-the-art techniques.