arXiv：cs.LG· Yule Sheng, Yue Yuan, Shan Shen, Daying Sun·· 4 小时前
HGRP：用于可布线性预测的异构图神经网络
HGRP: Heterogeneous Graph Neural Network for Routability Prediction
AI 导读
论文提出 HGRP，一种用于可布线性预测的异构图神经网络，将单元、网线和布线网格建模为不同类型的节点，显式表示拓扑连接、空间映射与网格邻接关系。在 CircuitNet 数据集上，HGRP 在拥塞预测任务中 F1-score 相对当前最优方法提升 18.69%。源码已公开，论文被 ASP-DAC2027 接收。
正文
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.
| Comments: | Accepted by ASP-DAC2027. Camera-Ready version |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.20209 [cs.LG] |
| (or arXiv:2610.20209v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20209 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shan Shen [view email]
[v1]
Wed, 14 Oct 2026 13:52:15 UTC (417 KB)
来源：arXiv：cs.LG · arxiv.org