aiqbench analysis
This paper introduces HGRP, a heterogeneous graph neural network designed to enhance routability prediction in electronic design automation by integrating logical topology, physical layout, and routing resources into a unified model. By explicitly modeling cells, nets, and grids as distinct node types, the approach achieves an 18.69% improvement in F1-score for congestion prediction tasks on the CircuitNet dataset. The work represents a significant methodological advance in applying machine learning to optimize chip placement workflows without requiring full routing simulations.
HGRP: Heterogeneous Graph Neural Network for Routability Prediction
arXiv:2610.20209v1 Announce Type: new Abstract: Routability prediction enables rapid evaluation of placement quality without routing, providing effective feedback for routability-driven placement optimization. Recent machine learning methods have achieved efficient routability prediction through data-driven approach. However, their prediction performance highly depends on the capability of input representations to capture essential circuit information. To address the limitations of existing circuit representations in simultaneously preserving logical topology, physical layout and routing resource information, this paper proposes HGRP, a heterogeneous graph neural network for routability prediction. HGRP models the cells, nets, and routing grids as distinct types of nodes and explicitly represents topological connectivity, spatial mapping, and grid adjacency, thereby constructing a heterogeneous graph representation that preserves multi-level circuit semantics. Based on the representation, a heterogeneous message passing mechanism is developed to enable information interaction and feature fusion among distinct types of nodes, effectively learning the complex correlations between circuit structures and routing behaviors. Experimental results on the CircuitNet dataset show that HGRP improves F1-score by 18.69\% relative improvements compared with the state-of-the-art in the congestion prediction task. The source code is publicly available at https://github.com/ShenShan123/HGPR.
Abstract:Routability prediction enables rapid evaluation of placement quality without routing, providing effective feedback for routability-driven placement optimization. Recent machine learning methods have achieved efficient routability prediction through data-driven approach. However, their prediction performance highly depends on the capability of input representations to capture essential circuit information. To address the limitations of existing circuit representations in simultaneously preserving logical topology, physical layout and routing resource information, this paper proposes HGRP, a heterogeneous graph neural network for routability prediction. HGRP models the cells, nets, and routing grids as distinct types of nodes and explicitly represents topological connectivity, spatial mapping, and grid adjacency, thereby constructing a heterogeneous graph representation that preserves multi-level circuit semantics. Based on the representation, a heterogeneous message passing mechanism is developed to enable information interaction and feature fusion among distinct types of nodes, effectively learning the complex correlations between circuit structures and routing behaviors. Experimental results on the CircuitNet dataset show that HGRP improves F1-score by 18.69\% relative improvements compared with the state-of-the-art in the congestion prediction task. The source code is publicly available at this https URL.
Accepted by ASP-DAC2027. Camera-Ready version
Machine Learning (cs.LG)
arXiv:2610.20209 [cs.LG]
(or arXiv:2610.20209v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.20209 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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