{"ID":2896896,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.05681","arxiv_id":"2507.05681","title":"GATMesh: Clock Mesh Timing Analysis using Graph Neural Networks","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.","short_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 sk...","url_abs":"https://arxiv.org/abs/2507.05681","url_pdf":"https://arxiv.org/pdf/2507.05681v1","authors":"[\"Muhammad Hadir Khan\",\"Matthew Guthaus\"]","published":"2025-07-08T05:18:42Z","proceeding":"cs.AR","tasks":"[\"cs.AR\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Graph Neural Network\"]","has_code":false}