MIHC: Multi-View Interpretable Hypergraph Neural Networks with Information Bottleneck for Chip Congestion Prediction

Zeyue Zhang, Heng Ping, Peiyu Zhang, Nikos Kanakaris, Xiaoling LU, Paul Bogdan, Xiongye Xiao

Keywords: Electronic Design Automation, Hypergraph Neural Networks, Interpretable Machine Learning, Information Bottleneck, Multi-view Learning, Chip Congestion Prediction

Abstract: With AI advancement and increasing circuit complexity, efficient chip design through Electronic Design Automation (EDA) is critical. Fast and accurate congestion prediction in chip layout and routing can significantly enhance automated design performance. Existing congestion modeling methods are limited by **(i)** ineffective processing and fusion of multi-view circuit data information, and **(ii)** insufficient reliability and interpretability in the prediction process. To address these challenges, We propose **M**ulti-view **I**nterpretable **H**ypergraph for **C**hip (**MIHC**), a trustworthy 'multi-view hypergraph neural network'-based framework that **(i)** processes both graph and image information in unified hypergraph representations, capturing topological and geometric circuit data, and **(ii)** implements a novel subgraph Information Bottleneck mechanism identifying critical congestion-correlated regions to guide predictions. This represents the first attempt to incorporate such interpretability into congestion prediction through informative graph reasoning. Experiments show our model reduces NMAE by 16.67% and 8.57% in cell-based and grid-based predictions on ISPD2015, and 5.26% and 2.44% on CircuitNet-N28, respectively, compared to state-of-the-art methods. Rigorous cross-design generalization experiments further validate our method’s capability to handle entirely unseen circuit designs.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 15513