GATMesh: Clock Mesh Timing Analysis using Graph Neural Networks

Fuente: arXiv
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Main Authors: Khan, Muhammad Hadir, Guthaus, Matthew
Format: Preprint
Published: 2025
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author Khan, Muhammad Hadir
Guthaus, Matthew
author_facet Khan, Muhammad Hadir
Guthaus, Matthew
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2507_05681
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle GATMesh: Clock Mesh Timing Analysis using Graph Neural Networks
Khan, Muhammad Hadir
Guthaus, Matthew
Hardware Architecture
Artificial Intelligence
Machine Learning
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.
title GATMesh: Clock Mesh Timing Analysis using Graph Neural Networks
topic Hardware Architecture
Artificial Intelligence
Machine Learning
url https://arxiv.org/abs/2507.05681