Dynamic Thermal-Aware Scheduling Using Physics-Informed POD-Galerkin Thermal Simulation Model for Multi-Core Processors : Invited Paper

Handling heavy computing tasks in real-time systems requires substantial power, leading to significant heat generation. This can cause serious thermal problems like rising temperatures, high thermal gradients, and hot spots. These issues can reduce chip performance, accelerate device aging, and cause premature failure. Thermal-Aware Scheduling (TAS) optimizes heat management to maintain a safe temperature. In this work, we evaluate RT-TAS, an advanced TAS algorithm using a steady-state thermal model, and compare it with our proposed dynamic physics-informed POD-based TAS algorithm. POD-TAS utilizes a reduced order thermal model with a dynamic predictive approach, allowing precise temperature control during task scheduling. This reduces peak temperatures and thermal variations. Our tests on a multi-core processor demonstrate that POD-TAS significantly improves thermal performance. It reduces the average spatial thermal variance by 6.18%, the variance of the maximum CPU temperature over time by 59.68%, and the variance of the average temperature by 13.05% due to its ability to maintain the chip temperature within a set threshold.