Lay-Net: Grafting Netlist Knowledge on Layout-Based Congestion Prediction

Congestion modeling is crucial for enhancing the routability of VLSI placement solutions. The underutilization of netlist information constrains the efficacy of existing layout-based congestion modeling techniques. We devise a novel approach that grafts netlist-based message passing into a layout-based model, thereby achieving a better knowledge fusion between layout and netlist to improve congestion prediction performance. The innovative heterogeneous message-passing paradigm more effectively incorporates routing demand into the model by considering connections between cells, overlaps of nets, and interactions between cells and nets. Leveraging multi-scale features, the proposed model effectively captures connection information across various ranges, addressing the issue of inadequate global information present in existing models. Using contrastive learning and mini-Gnet techniques allows the model to learn and represent features more effectively, boosting its capabilities and achieving superior performance. Extensive experiments demonstrate a notable performance enhancement of the proposed model compared to existing methods.Our code is available at: https://github.com/lanchengzou/congPred.