English(EN) HGRP: Heterogeneous Graph Neural Network for Routability Prediction

新型HGRP模型将电路可布线性预测提升18.69%

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了HGRP，这是一种新颖的异构图神经网络，旨在提高电路可布线性的预测能力。该模型通过将单元格、网络和布线网格表示为不同的节点类型，有效地捕获了多级电路语义，从而实现了更好的信息交互和特征融合。在CircuitNet数据集上的实验表明，HGRP显著提高了拥塞预测性能，与现有最先进方法相比，F1分数相对提高了18.69%。HGRP的源代码是公开可用的。 AI

影响这一新模型可以通过提供更快、更准确的布局质量反馈来加速电子设计自动化，从而可能简化芯片设计工作流程。

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arXiv cs.LG TIER_1 English(EN) · Yule Sheng, Yue Yuan, Shan Shen, Daying Sun · 2026-10-15 04:00

HGRP：用于可布线性预测的异构图神经网络

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 pr…

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HGRP：用于可布线性预测的异构图神经网络

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