Machine Learning application for early power analysis accuracy improvement

In this paper, we introduce a machine learning (ML) application that accurately estimates the switching power of the cells without needing the SPEF file (SPEF less PA flow). Three ML models (multi-linear regression, random forest and decision tree) were trained and tested on different industrial designs at 7nm technology. They are trained using different cells’ properties available, SPEF, and SPEF-less power numbers to accurately predict the switching power and eliminate the need for the SPEF file.

With this new ML approach, we were able to reduce the SPEF-less flow’s average cell switching power error from 34 percent to 8 percent.