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

Zeyue Zhang (Renmin University of China) · Heng Ping (University of Southern California) · Peiyu Zhang (University of Southern California) · Nikos Kanakaris (Amazon) · Xiaoling LU (Renmin University of China) · Paul Bogdan (University of Southern California) · Xiongye Xiao (University of Tennessee, Knoxville)

automated design performancecell-based predictionscircuit designscongestion predictioncross-design generalizationelectronic design automationgeometric datagraph reasoninggrid-based predictionshypergraph neural network interpretabilitymulti-view circuit datanmae reductionsubgraph information bottlenecktopological data

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.