A Parallel Simulation Framework Incorporating Machine Learning-Based Hotspot Detection for Accelerated Power Grid Analysis

Power grid analysis is essential for integrated circuit design, especially as the scale extends to billions of nodes, making precise calculations across the entire spatial domain prohibitively expensive. Domain decomposition methods (DDM) facilitate block-wise parallel analysis of power distribution networks (PDN) through segmentation, yet they still face bottlenecks in computational load balance during parallel processing. Considering the vastness of PDN structures and the emphasis on verifying areas prone to significant IR drop (i.e., hotspot) rather than those less affected, our research introduces a machine learning (ML)-based method for detecting PDN hotspots. This method focuses on accurately identifying areas within sub-blocks where the hotspot intensity is high. We then propose a novel parallel approximation computing approach that integrates hotspot detection, enabling approximations in regions not marked as hotspots. This strategy optimizes computational resources and effectively accelerates analysis. Our experimental results demonstrate that the hotspot detection model achieves an average accuracy of 94.39%. The approximation strategy for non-hotspot sub-blocks results in 0.56-1.4% mean relative error, while achieving an average computational speedup of 3.21× over the CK-TSO parallel solver and 2.21× over the conventional DDM-based solver.