Adaptive Behavior-Driven Thermal Management Framework in Heterogeneous Multicore Processors

In modern computing environments, heterogeneous multi-core processors are increasingly used to balance performance and energy efficiency. However, as processor architectures become more complex and workloads increase, traditional Dynamic Voltage and Frequency Scaling (DVFS) methods face challenges in ensuring thermal stability without significant performance trade-offs in high-performance computing applications. This letter proposes a scalable adaptive thermal management framework that leverages phase-based thermal detection of workloads alongside adaptive migration techniques. The framework dynamically detects the thermal phases of running applications and optimally allocates tasks and threads across cores based on thermal characteristics and workload demands. The proposed framework on the Apalis iMX8, evaluated using the PARSEC benchmark, reduces average and peak temperatures by 16.7∘C and 32.5∘C, respectively, while enhancing performance by 13.5% compared to DVFS-based DTM techniques. It also outperforms methods such as CARTAD and PTS, demonstrating superior efficiency and adaptability in thermal management.