Graph Neural Networks (GNNs) are increasingly explored for physical design analysis in Electronic Design Automation, particularly for modeling Clock Tree Synthesis behavior such as clock skew and buffering complexity. However, practical deployment remains limited due to the prohibitive memory and runtime cost of operating on raw gate-level netlists. Graph coarsening is commonly used to improve scalability, yet its impact on CTS-critical learning objectives is not well characterized. This paper introduces CTS-Bench, a benchmark suite for systematically evaluating the trade-offs between graph coarsening, prediction accuracy, and computational efficiency in GNN-based CTS analysis. CTS-Bench consists of 4,860 converged physical design solutions spanning five architectures and provides paired raw gate-level and clustered graph representations derived from post-placement designs. Using clock skew prediction as a representative CTS task, we demonstrate a clear accuracy-efficiency trade-off. While graph coarsening reduces GPU memory usage by up to 17.2x and accelerates training by up to 3x, it also removes structural information essential for modeling clock distribution, frequently resulting in negative $R^2$ scores under zero-shot evaluation. Our findings indicate that generic graph clustering techniques can fundamentally compromise CTS learning objectives, even when global physical metrics remain unchanged. CTS-Bench enables principled evaluation of CTS-aware graph coarsening strategies, supports benchmarking of GNN architectures and accelerators under realistic physical design constraints, and provides a foundation for developing learning-assisted CTS analysis and optimization techniques.

翻译：图神经网络（GNNs）在电子设计自动化的物理设计分析中日益受到关注，尤其在建模时钟树综合行为（如时钟偏斜和缓冲器复杂度）方面。然而，由于在原始门级网表上运行所需的内存和运行时成本过高，实际部署仍受到限制。图粗化技术常被用于提升可扩展性，但其对时钟树综合关键学习目标的影响尚未得到充分表征。本文提出CTS-Bench，这是一个用于系统评估基于GNN的时钟树综合分析中图粗化、预测精度与计算效率之间权衡的基准测试套件。CTS-Bench包含涵盖五种架构的4,860个收敛物理设计解，并提供从布局后设计导出的成对原始门级图与聚类图表示。以时钟偏斜预测作为代表性时钟树综合任务，我们揭示了明显的精度-效率权衡：图粗化虽能降低GPU内存使用达17.2倍、加速训练达3倍，但同时也移除了对建模时钟分布至关重要的结构信息，在零样本评估中常导致负$R^2$分数。我们的研究表明，即使全局物理指标保持不变，通用图聚类技术也可能从根本上损害时钟树综合学习目标。CTS-Bench支持对时钟树综合感知的图粗化策略进行原则性评估，助力在真实物理设计约束下对GNN架构与加速器进行基准测试，并为开发学习辅助的时钟树综合分析与优化技术奠定基础。