A Statistical Static Timing Analysis Algorithm Based On Graph Neural Network
Yufan Chen, Leyun Tian, Yuyang Ye, Chenpu Shi, Hao Yan · 2025
Satistical static timing analysis (SSTA) struggles with nonlinear MAX operations, which is crucial in block based statistical timing analysis. Most existing methods either incur high computational costs or rely on inaccurate approximations. In this work, we propose a SSTA algorithm based on graph neural network (GNN) to deal with non-Gaussian while balancing speed by using its node regression function. GNN eliminate the traversal propagation through message passing mechanisms and can simulate the nonlinear behavior of MAX operation through its attention mechanisms. Experimental results demonstrate that our model outperforms the First-order delay model and Skew-normal delay model in terms of accuracy, especially when skewness has a significant impact, while its time overhead is on the same order of magnitude as First-order model. Taking into account both accuracy and runtime, our proposed model has significant computational efficiency advantages while ensuring high prediction accuracy.