Routability Prediction using Deep Hierarchical Classification and Regression

Abstract

Routability prediction can forecast the locations where design rule violations occur without routing and thus can speed up the design iterations by skipping the time-consuming routing tasks. This paper investigated (i) how to predict the routability on a continuous value and (ii) how to improve the prediction accuracy for the minority samples. We propose a deep hierarchical classification and regression (HCR) model that can detect hotspots with the number of violations. The hierarchical inference flow can prevent the model from overfitting to the majority samples in imbalanced data. In addition, we introduce a training method for the proposed HCR model that uses Bayesian optimization to find the ideal modeling parameters quickly and incorporates transfer learning for the regression model. We achieved an R2 score of 0.71 for the regression and increased the Fl score in the binary classification by 94% compared to previous work [6].