GATMesh: Clock Mesh Timing Analysis using Graph Neural Networks

Muhammad Hadir Khan; Matthew Guthaus

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arXiv · 2507.05681

GATMesh: Clock Mesh Timing Analysis using Graph Neural Networks

Muhammad Hadir Khan·Matthew Guthaus

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Abstract

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.

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Muhammad Hadir Khan, Matthew Guthaus. 2025-07-08. GATMesh: Clock Mesh Timing Analysis using Graph Neural Networks. https://arxiv.org/abs/2507.05681

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