Pre Synthesis and Post Synthesis Power Estimation of VLSI Circuits Using Machine Learning Approach

In today's world, people need sleeker devices with better functionality and longer battery life. This can be achieved by integrating more components onto smaller chips, resulting in a shift to low-geometry chip design. However, power dissipation due to dynamic and static currents is more prominent in all ICs, resulting in an increase in overall power consumption. Estimating power dissipation early will provide more accurate usage of power pads/strips and help floor plan engineers do power planning efficiently. As you provide more details about your design characteristics, the estimation of power will be accurate. The major focus of this work is to give an alternative solution to predict the power dissipation of integrated circuits using a machine learning approach in both pre and post layout. The proposed work uses supervision models and algorithms like Linear regression, KNN, SVM, and RF for power prediction and a comparative study is made between power estimates made using ML algorithms and by the Cadence EDA tool for a particular technology for various bench circuits. The average error is less than 4% when we compare the estimated power using ML and by using the Cadence EDA tool and shows that for estimation of power in integrated circuits, Random Forest is a well-suited algorithm with an error percentage varying from 2 to 4.