Dynamic Thermal Management with Proactive Fan Speed Control Through Reinforcement Learning
Arman Iranfar1,a, Federico Terraneo2,e, Gabor Csordas1,b, Marina Zapater1,c, William Fornaciari2,f and David Atienza1,d
1Embedded Systems Laboratory (ESL), Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland
aarman.iranfar@epfl.ch
bgabor.csordas@epfl.ch
cmarina.zapater@epfl.ch
ddavid.atienza@epfl.ch
2Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Italy
efederico.terraneo@polimi.it
fwilliam.fornaciari@polimi.it
ABSTRACT
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.
