An Evaluation Framework for Dynamic Thermal Management Strategies in 3D MultiProcessor System-on-Chip Co-Design

Dynamic thermal management (DTM) has been widely adopted to improve the energy efficiency, reliability, and performance of modern Multi-Processor SoCs (MPSoCs). However, the evolving industry trends and heterogeneous architecture designs have introduced significant challenges in state-of-the-art DTM methods. Specifically, the emergence of heterogeneous design has led to increased localized and non-uniform hotspots, necessitating accurate and responsive DTM strategies. Additionally, the increased number of cores to be managed requires the DTM to optimize and coordinate the whole system. However, existing methodologies fail in both precise thermal modeling in localized hotspots and fast architecture simulation. To tackle these existing challenges, we first introduce the latest version of 3D-ICE 3.1, with a novel non-uniform thermal modeling technique to support customized discretization levels of thermal grids. 3D-ICE 3.1 improves the accuracy of thermal analysis and reduces simulation overhead. Then, in conjunction with an efficient and fast offline application profiling strategy utilizing the architecture simulator gem5-X, we propose a novel DTM evaluation framework. This framework enables us to explore novel DTM methods to optimize the energy efficiency, reliability, and performance of contemporary 3D MPSoCs. The experimental results demonstrate that 3D-ICE 3.1 achieves high accuracy, with only 0.3K mean temperature error. Subsequently, we evaluate various DTM methods and propose a Multi-Agent Reinforcement Learning (MARL) control to address the demanding thermal challenges of 3D MPSoCs. Our experimental results show that the proposed DTM method based on MARL can reduce power consumption by 13% while maintaining a similar performance level to the comparison methods.