论文标题
LHNN：用于VLSI拥塞预测的晶格超图神经网络
LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction
论文作者
论文摘要
放置解决方案的精确拥塞预测在电路放置中起着至关重要的作用。这项工作提出了晶格超图（LH-graph），这是一种新型电路的图形公式，该图表在整个学习过程中保留了NetList数据，并启用了拥塞信息，以几何和拓扑传播。基于该公式，我们进一步开发了一个异质图神经网络体系结构LHNN，从而将路由需求回归构成以支持拥塞点分类。与U-NET和F1分数相比，LHNN不断提高35％以上。我们预计我们的工作将使用机器学习进行拥塞预测强调基本程序。
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% improvements compared with U-nets and Pix2Pix on the F1 score. We expect our work shall highlight essential procedures using machine learning for congestion prediction.
