A Physical and Timing Aware Placement Optimization Framework Based on Graph Neural Network
Wenjie Ding, Zhanhua Zhang, Guoqing He, Peng Cao · 2024
Timing-driven placement is crucial in physical design flow with significant impact on later routability and ultimate manufacturability, which may deviate from finding the optimal solution and/or lead to unnecessary iterations, suffering from interleaved optimization steps and the corresponding inaccurate timing estimation. To solve this issue, we propose a Physical and Timing Aware framework with Graph Neural Network, PTA-GNN, which provides the candidate gate sizing and buffer insertion solutions as well as the timing constraint for potential violated paths as guidance to improve placement quality significantly. Experimental results on the OpenCores benchmarks with 22nm technology demonstrate that the proposed placement optimization framework achieves up to 89.09% worst negative slack (WNS), 55.47% total negative slack (TNS) improvement and 25.36% reduction on the number of violating paths (#VP). Our framework benefits the later routing stage with 2.19% wire-length decrease and 22% runtime reduction compared to standard physical design flow.