Pre-route timing prediction and optimization with graph neural network models

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In recent years, the application of deep learning (DL) models has sparked considerable interest in timing prediction within the place-and-route (P&R) flow of IC chip design. Specifically, at the pre-route stage, an accurate prediction of post-route timing is challenging due to the lack of sufficient physical information. However, achieving precise timing prediction significantly accelerates the design closure process, saving considerable time and effort. In this work, we propose pre-route timing prediction and optimization framework with graph neural network (GNN) models combined with convolution neural network (CNN). Our framework is divided into two main stages, each of which is further subdivided into smaller steps. Precisely, our GNN-driven arc delay/slew prediction model is divided into two levels: in level-1, it predicts net resistance (net R) and net capacitance (net C) using GNN while the arc length is predicted using CNN. These predictions are hierarchically passed on to level-2 where delay/slew is estimated with our GNN based prediction model. The timing optimization model utilizes the precise delay/slew predictions obtained from the GNN-driven prediction model to accurately set the path margin during the timing optimization stage. This approach effectively reduces unnecessary turn-around iterations in the commercial EDA tools. Experimental results show that by using our proposed framework in P&R, we are able to improve the pre-route prediction accuracy by 42%/36% on average on arc delay/slew, and improve timing metrics in terms of WNS, TNS, and the number of timing violation paths by 77%, 77%, and 64%, which are an increase of 32%/35% on arc delay/slew and 30%, 20% and 31% on timing optimization compared with the existing DL prediction model.