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

With the advancement of artificial intelligence (AI) and increasing integrated circuit (IC) design complexity, efficient chip design through electronic design automation (EDA) has become 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 inter-pretability in the prediction process. To address these challenges, we propose the M ulti-view I nterpretable H ypergraph for C hip ( MIHC ), a trustworthy multi-view hypergraph neural network framework that (i) processes both graph and image information in unified hypergraph representations, capturing topological and geometric circuit data; (ii) implements a novel subgraph Information Bottleneck mechanism, identifying critical congestion-correlated regions to guide predictions. This work is the first attempt to incorporate such interpretability into congestion prediction through informative graph reasoning. Experiments show that the MIHC method 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.