Regulating CPU Temperature With Thermal-Aware Scheduling Using a Reduced Order Learning Thermal Model
Anthony Dowling, Lin Jiang, Ming-Cheng Cheng, Yu Liu

TL;DR
This paper introduces POD-TAS, a thermal-aware scheduling algorithm for multi-core CPUs that uses a reduced order thermal model to effectively manage heat, outperforming existing methods in reducing peak temperatures and thermal variance.
Contribution
The paper presents a novel thermal-aware scheduling algorithm, POD-TAS, based on a reduced order thermal model, and demonstrates its superior thermal management performance over existing algorithms.
Findings
POD-TAS reduces peak thermal variance by 53.0% with 4 benchmarks.
POD-TAS decreases peak chip temperature by 29.01%.
Compared to RT-TAS, POD-TAS lowers peak spatial variance by 29.57%.
Abstract
Modern real-time systems utilize considerable amounts of power while executing computation-intensive tasks. The execution of these tasks leads to significant power dissipation and heating of the device. It therefore results in severe thermal issues like temperature escalation, high thermal gradients, and excessive hot spot formation, which may result in degrading chip performance, accelerating device aging, and premature failure. Thermal-Aware Scheduling (TAS) enables optimization of thermal dissipation to maintain a safe thermal state. In this work, we implement a new TAS algorithm, POD-TAS, which manages the thermal behavior of a multi-core CPU based on a defined set of states and their transitions. We compare the performances of a dynamic RC thermal circuit simulator (HotSpot) and a reduced order Proper Orthogonal Decomposition (POD)-based thermal model and we select the latter for…
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Taxonomy
TopicsFerroelectric and Negative Capacitance Devices · Semiconductor materials and devices · Advancements in Semiconductor Devices and Circuit Design
