Graph Neural Network based Initialization for Timing Driven Placement

Timing-driven placement is very important to achieve timing closure especially as designs become increasingly complex. This article presents a novel Timing-Driven Placement (TDP) framework that integrates a graph convolutional network (GCN), Dirichlet boundary conditions, and a nonlinear placement engine to optimize placement quality with timing awareness throughout the flow. The proposed methodology begins by clustering components based on their interconnection topology, while Dirichlet boundary conditions are applied to handle fixed components such as IOs and macros. This yields a reduced graph with minimized inter-cluster connectivity, simplifying timing optimization. A GCN is then trained to learn a generalized and optimized mapping from circuit connectivity to physical wirelength. To improve early-stage timing estimation, virtual buffers are inserted prior to Static Timing Analysis (STA) to eliminate maximum capacitance violations. With this improved timing fidelity, STA provides pin-level slack, which is then used to dynamically adjust interconnection weights, guiding the placement of timing-critical components toward improved timing closure. Experimental results on ICCAD2015 contest benchmarks demonstrate that our algorithm can improve worse negative slack and total negative slack by 6% compared to the state-of-the-art method.