부산대학교 도서관

We propose a multicorner multistage timing analysis prediction framework using a generalized linear model with latent features. We then further improve such methods using kernel trick extension, transfer learning with knowledge from previous designs, and multioutput feature engineering to deliver state-of-the-art (SOTA) prediction accuracy with very limited training data. Most importantly, our method is equipped with a Bayesian decision strategy to deliver reliable predictions with accuracy close to 100%, pushing the frontier of the machine-learning-based static timing analysis (STA) for practical implementation in the industry environment, where reliability is highly desired. Experimental results show that the accuracy of our proposed method outperforms the SOTA competitors by up to 4x and can improve prediction accuracy to 100% with little extra STA executions.