Accurate early congestion prediction can prevent unpleasant surprises at the routing stage, playing a crucial character in assisting designers to iterate faster in VLSI design cycles. In this paper, we introduce a novel strategy to fully incorporate topological and geometrical features of circuits by making several key designs in our network architecture. To be more specific, we construct two individual graphs (geometry-graph, topology-graph) with distinct edge construction schemes according to their unique properties. We then propose a dual-branch network with different encoder layers in each pathway and aggregate representations with a sophisticated fusion strategy. Our network, named HybridNet, not only provides a simple yet effective way to capture the geometric interactions of cells, but also preserves the original topological relationships in the netlist. Experimental results on the ISPD2015 benchmarks show that we achieve an improvement of 10.9% compared to previous methods.

翻译：准确的前期拥塞预测能够避免布線阶段出现意外问题，在协助设计者加速VLSI设计周期迭代中起着关键作用。本文提出了一种新颖策略，通过网络架构中的若干关键设计，全面融合电路的拓扑特征与几何特征。具体而言，我们根据几何图与拓扑图各自独特的属性，构建了具有不同边构造方案的两类独立图结构。进而提出一种双分支网络，每条路径采用不同的编码器层，并通过精密的融合策略聚合表征。所提出的HybridNet不仅提供了一种简单有效的方式来捕捉单元的几何交互，还保留了网表中原始的拓扑关系。在ISPD2015基准测试上的实验结果表明，与先前方法相比，我们实现了10.9%的性能提升。