Dynamic thermal management for multimedia applications using machine learning

Multimedia applications are expected to form the largest portion of workload in general purpose PC and portable devices. The ever-increasing computation intensity of multimedia applications elevates the processor temperature and consequently impairs the reliability and performance of the system. In this paper, we propose to perform dynamic thermal management using reinforcement learning algorithm for multimedia applications. The proposed learning model does not need any prior knowledge of the workload information or the system thermal and power characteristics. It learns the temperature change and workload switching patterns by observing the temperature sensor and event counters on the processor, and finds the management policy that provides good performance-thermal tradeoff during the runtime. We validated our model on a Dell personal computer with Intel Core 2 processor. Experimental results show that our approach provides considerable performance improvements with marginal increase in the percentage of thermal hotspot comparing to existing workload phase detection approach.