Machine Learning Based Power Estimation for CMOS VLSI Circuits

Nowadays, machine learning (ML) algorithms are receiving massive attention in most of the engineering application since it has capability in complex systems modeling using historical data. Estimation of power for CMOS VLSI circuit using various circuit attributes is proposed using passive machine learning-based technique. The proposed method uses supervised learning method, which provides a fast and accurate estimation of power without affecting the accuracy of the system. Power estimation using random forest algorithm is relatively new. Accurate estimation of power of CMOS VLSI circuits is estimated by using random forest model which is optimized and tuned by using multiobjective NSGA-II algorithm. It is inferred from the experimental results testing error varies from 1.4% to 6.8% and in terms of and Mean Square Error is 1.46e-06 in random forest method when compared to BPNN. Statistical estimation like coefficient of determination (R) and Root Mean Square Error (RMSE) are done and it is proven that random Forest is best choice for power estimation of CMOS VLSI circuits with high coefficient of determination of 0.99938, and low RMSE of 0.000116.

Automated summary

The study introduces a method for estimating the power of CMOS VLSI circuits using machine learning, specifically the random forest algorithm, which is optimized by the multiobjective NSGA-II algorithm. Experimental results demonstrate the high accuracy and effectiveness of the random forest method in power estimation for CMOS VLSI circuits.