标题： GATMesh：使用图神经网络的时钟网格定时分析 显示英文标题

标题： GATMesh: Clock Mesh Timing Analysis using Graph Neural Networks

摘要： 时钟网格在高性能VLSI系统中至关重要，用于最小化偏斜并处理PVT变化，但由于收敛路径、多源驱动和输入网格缓冲器偏斜，分析它们很困难。SPICE仿真准确但缓慢；然而简化的模型会遗漏如转换时间和输入偏斜等关键效应。我们提出了GATMesh，这是一种基于图神经网络（GNN）的框架，将时钟网格建模为具有增强结构和物理特征的图。在SPICE数据上训练，GATMesh在未见过的基准测试中实现了5.27ps的平均延迟误差，同时比多线程SPICE仿真快47146倍。 显示英文摘要

摘要： Clock meshes are essential in high-performance VLSI systems for minimizing skew and handling PVT variations, but analyzing them is difficult due to reconvergent paths, multi-source driving, and input mesh buffer skew. SPICE simulations are accurate but slow; yet simplified models miss key effects like slew and input skew. We propose GATMesh, a Graph Neural Network (GNN)-based framework that models the clock mesh as a graph with augmented structural and physical features. Trained on SPICE data, GATMesh achieves high accuracy with average delay error of 5.27ps on unseen benchmarks, while achieving speed-ups of 47146x over multi-threaded SPICE simulation.