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Precise congestion prediction from a placement solution plays a crucial rolein circuit placement. This work proposes the lattice hypergraph (LH-graph), anovel graph formulation for circuits, which preserves netlist data during thewhole learning process, and enables the congestion information propagatedgeometrically and topologically. Based on the formulation, we further developeda heterogeneous graph neural network architecture LHNN, jointing the routingdemand regression to support the congestion spot classification. LHNNconstantly achieves more than 35% improvements compared with U-nets and Pix2Pixon the F1 score. We expect our work shall highlight essential procedures usingmachine learning for congestion prediction.

LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction