LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction

03/24/2022

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by   Bowen Wang, et al.

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Precise congestion prediction from a placement solution plays a crucial role in circuit placement. This work proposes the lattice hypergraph (LH-graph), a novel graph formulation for circuits, which preserves netlist data during the whole learning process, and enables the congestion information propagated geometrically and topologically. Based on the formulation, we further developed a heterogeneous graph neural network architecture LHNN, jointing the routing demand regression to support the congestion spot classification. LHNN constantly achieves more than 35 on the F1 score. We expect our work shall highlight essential procedures using machine learning for congestion prediction.