OS-Level PMC-Based Runtime Thermal Control for ARM Mobile CPUs

In order to improve performance and avoid overheating on mobile devices, precise thermal control with low overhead is crucial. To achieve this, we propose incorporating a PMC-based power model into thermal control, which enables more accurate evaluation of the CPU’s power consumption. We demonstrate the plausibility of this approach using polynomial regression based on Moore’s Law. Additionally, we introduce a lightweight PMCs Sampling method that can collect multiple PMCs at once in the kernel space, reducing sampling overhead. By replacing the utilization-based model in the original IPA with a PMC-based power model, we realize the PMC-based IPA governor that can be ported to real mobile devices. After updating the thermal control governor in the Linux kernel, we perform tests on our PMC-based IPA using a mobile phone device. We compare it with Stepwise and IPA, which are commonly used in current mobile phone systems. We choose the CPU-incentive workbench, I/O-incentive workbench, and CPU & I/O-incentive hybrid workbench as workloads. The results show that PMC-based IPA effectively reduces power consumption while improving performance. In particular, during the CPU & I/O-incentive hybrid experiment, where CPU-intensive and I/O-intensive tasks are executed alternately, PMC-based IPA reduces the running time by 10.0% and energy consumption by 16.6% compared to the original IPA. In order to verify the benefits of PMC-based IPA, mobile phone testing software AI Bench and Antutu are utilized. The results show that our scheme is able to control temperature more precisely than IPA and achieved a better score while consuming less energy, particularly during AI computing. These experiment results suggest that PMC-based IPA is valuable for practical use.