Machine-Learning-Based Multi-Corner Timing Prediction for Faster Timing Closure

Abstract

For the purpose of fixing timing violations, static timing analysis (STA) of full-corners is repeatedly executed, which is time-consuming. Given a timing path, timing results at some corners (“dominant corners”) are utilized to predict timing at other corners (“non-dominant corners”), which can greatly shorten the runtime of STA. However, the huge number of combinations of the dominant corners and the wide difference in prediction accuracy make it difficult to apply multi-corner timing prediction to chip industrial design. In this paper, we propose a dominant corner selection strategy to quickly determine the dominant corner combination with high prediction accuracy, along with which a new multi-corner timing prediction process is established to speed up STA. Experimental results show that our method can not only effectively accelerate STA, but also ensure the high prediction accuracy of the prediction timing. On the public ITC’99 benchmark, the prediction accuracy of the dominant corner combination selected by the proposed method is up to 98.2%, which is an improvement of 15% compared to the state-of-the-art method. For industrial application, we apply our method by using timing results on only 2 dominant corners to predict the other 12 non-dominant corners, which accelerates the runtime of the timing closure process by more than 2×.