Dynamic Thermal Management with Proactive Fan Speed Control Through Reinforcement Learning

Dynamic Thermal Management (DTM) has become a major challenge since it directly affects Multiprocessors Systems-on-chip (MPSoCs) performance, power consumption, and reliability. In this work, we propose a transient fan model, enabling adaptive fan speed control simulation for efficient DTM. Our model is validated through a thermal test chip achieving less than 2◦ C error in the worst case. With multiple fan speeds, however, the DTM design space grows significantly, which can ultimately make conventional solutions impractical. We address this challenge through a reinforcement learning-based solution to proactively determine the number of active cores, operating frequency, and fan speed. The proposed solution is able to reduce fan power by up to 40% compared to a DTM with constant fan speed with less than 1% performance degradation. Also, compared to a state-of-the-art DTM technique our solution improves the performance by up to 19% for the same fan power.

Published in:
[Proceedings Design, Automation and Test in Europe Conference and Exhibition]
Presented at:
Design, Automation, and Test in Europe - DATE2020, Grenoble, France, March 9-13, 2020

Note: The status of this file is: Anyone

 Record created 2019-11-27, last modified 2020-10-25

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