Machine Learning Application For Early Power Analysis Accuracy Improvement

A case study for cells switching power.


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. To read more, click here.

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