A GNN-Based Placement Optimization Guidance Framework by Physical and Timing Prediction
Peng Cao, Zhi Li, Wenjie Ding · Electronics · 2025
Placement is crucial in physical design flow with significant impact on later routability and ultimate manufacturability in terms of performance, power, and area (PPA), which may deviate from finding the optimal solution and/or lead to unnecessary iterations suffering from interleaved optimization steps and inaccurate PPA estimation. To solve this issue, we propose a physical- and timing-related placement optimization guidance framework which provides candidate gate sizing and buffer insertion solutions as well as a path group for potential violated paths based on graph neural networks (GNNs) to improve placement quality significantly and efficiently. Experimental results on the OpenCores benchmarks with 22 nm technology demonstrate that the proposed placement optimization guidance framework achieves up to 35.66% and 43.51% worst negative slack (WNS) and total negative slack (TNS) improvement and 52.17% reduction in the number of violating paths (NVP), which is beneficial to later routing stages with 2.33% wirelength decrease.