Graph Neural Networks for Efficient Clock Tree Synthesis Optimization in Complex SoC Designs
Jiang Wu, Chunhe Ni, Hongbo Wang, Jingyi Chen · Applied and Computational Engineering · 2025
This paper presents a novel graph neural network (GNN) based framework for efficient clock tree synthesis (CTS) optimization in complex System-on-Chip designs. As technology nodes advance to 5nm and below, traditional CTS methodologies face significant challenges in optimizing power, performance, and skew metrics while managing exponentially growing design complexity. We propose a specialized GNN architecture incorporating bidirectional message passing mechanisms and attention components to effectively capture critical clock network characteristics. The framework implements a multi-objective optimization approach that simultaneously addresses power consumption, insertion delay, and clock skew constraints through reinforcement learning techniques. Our hybrid methodology integrates GNN-based predictions with conventional CTS algorithms, achieving a synergistic workflow that preserves design rule compliance while enhancing optimization capabilities. Experimental evaluation across multiple benchmark circuits and industrial SoC designs demonstrates average reductions of 8.7% in clock power, 6.3% in maximum skew, and 1.8% in insertion delay compared to state-of-the-art commercial tools, while simultaneously reducing runtime by 56.2%. The performance advantages scale favorably with increasing design complexity, showing sublinear computational growth compared to the superlinear scaling of traditional methods. The framework demonstrates robust performance across diverse application domains including mobile processors, automotive controllers, and AI accelerators, validating its practical applicability in advanced technology nodes.