Pre-Routing Slack Prediction Based on Graph Attention Network

Static Timing Analysis (STA) plays a crucial role in realizing timing convergence of integrated circuits. In recent years, there has been growing research on pre-routing timing prediction using Graph Neural Networks (GNNs). However, existing approaches struggle with scalability on large graphs and lack generalizability to new designs, limiting their applicability to large-scale, complex circuit problems. To address this issue, this paper proposes a timing engine based on Graph Attention Network (GAT) to predict the slack of timing endpoints. Firstly, our model computes net embeddings for each node prior to training using a gated self-attention module. Subsequently, inspired by the Nonlinear Delay Model (NLDM), the node embeddings are propagated through multiple levels by alternately applying net propagation layers and cell propagation layers. Evaluated on 21 real circuits, the framework achieved a 16.62% improvement in average R2 score for slack prediction and a 15.55% reduction in runtime compared to the state-of-the-art (SOTA) method.