HGRP: Heterogeneous Graph Neural Network for Routability Prediction

Key points

• Routability prediction enables rapid evaluation of placement quality without routing, providing effective feedback for routability-driven placement optimization.1
• 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.1
• 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.1
• 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.1

Sources (1)

• [1]HGRP: Heterogeneous Graph Neural Network for Routability Prediction
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 01:52 PM

  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.

  Routability prediction enables rapid evaluation of placement quality without routing, providing effective feedback for routability-driven placement optimization.

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