Graph Neural Networks for Timing Optimization in Advanced Node Placement

Abstract

The escalating complexity of modern integrated circuit design demands innovative approaches to address timing optimization challenges in advanced technology nodes. Graph Neural Networks (GNNs) have emerged as a transformative paradigm for modeling circuit representations and optimizing placement decisions. This paper presents a comprehensive investigation of GNN applications in timing-driven placement optimization for sub-10nm process technologies. We propose a novel framework that leverages GNN architectures to encode circuit connectivity patterns, predict timing metrics, and guide placement algorithms toward solutions that minimize critical path delays while maintaining acceptable wirelength overhead. Our methodology employs a two-stage GNN model integrating global placement refinement with local timing optimization subroutines. The framework captures spatial dependencies between circuit components through message passing mechanisms while incorporating timing constraints directly into the optimization objective. Experimental evaluations on industry benchmark circuits demonstrate that GNN-based timing optimization achieves 18-24% reduction in worst negative slack compared to conventional analytical placement methods, with runtime improvements of 3-5x over traditional static timing analysis iterations. The proposed approach maintains placement quality metrics including wirelength increase below 7% and demonstrates robust convergence across diverse circuit topologies ranging from processor cores to memory controllers. This research establishes GNNs as viable alternatives to conventional timing-driven placement algorithms and opens new directions for machine learning integration in electronic design automation workflows.