Young Geun Kim, Minyong Kim, Joonho Kong, and Sung Woo Chung, "An Adaptive Thermal Management Framework for Heterogeneous Multi-core Processors", IEEE Transactions on Computers, vol. 69, no. 6, pp. 894-906, June 2020.
Off-the-shelf embedded systems have adopted heterogeneous multi-core processors which have high-performance big cores and low-power small cores. Though there are two different types of cores in heterogeneous multi-core processors, conventional DVFS (Dynamic Voltage and Frequency Scaling)-based DTM (Dynamic Thermal Management) techniques do not utilize the different types of cores to cool down hot cores. Rather, they primarily reduce the voltage and frequency of the hot cores, leading to performance degradation. In this paper, we propose a novel adaptive DTM framework for heterogeneous multi-core processors, which utilizes the big and small cores to prevent performance degradation. Our proposed framework exploits two migration-based DTM techniques: 1) a technique (denoted as Migrationbig↔big) that migrates applications from hot big cores (big cores whose temperature is above a pre-defined threshold) to cold big cores (big cores whose temperature is below the threshold) and 2) a technique (denoted as Migrationbig↔small) that migrates all applications from the big cores to the small cores. In case of thermal emergency of the big cores, our proposed framework checks the number of cold big cores. When there exist available cold big cores, our proposed framework employs Migrationbig↔big to cool down the hot big cores while not reducing the big core frequency. On the other hand, when there does not exist any available cold big core, our proposed framework employs one between Migrationbig↔small and a DVFS-based DTM technique, which is expected to result in better performance. In our experiments on an embedded development board, our proposed framework improves the average performance by 8.9%, compared to ARM’s DVFS-based IPA (Intelligent Power Allocation), satisfying thermal constraints. Our framework also improves the average performance by 10.4%, compared to a state-of-the-art predictive DVFS-based DTM technique.