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

Congestion modeling is a key point for improving the routability of VLSI placement solutions. The underuti-lization of netlist information limits the performance of ex-isting layout-based congestion modeling methods. Combining the knowledge from netlist and layout, we graft netlist-based message passing on a layout-based model to achieve better congestion prediction performance. The novel heterogeneous message-passing paradigm better embeds the routing demand into the model by considering both connections between cells and overlaps of nets. With the help of multi-scale features, the proposed model can effectively capture connection information across different ranges, overcoming the problem of insufficient global information in existing models. Based on the advancements, the proposed model achieves significant improvement compared with existing methods.