Basic experimentation on accuracy of power estimation for CMOS VLSI circuits

2002 ◽  
Author(s):  
T. Ishihara ◽  
H. Yasuura
Keyword(s):  
Power Estimation ◽  
Vlsi Circuits ◽  
Cmos Vlsi

MAXIMUM POWER ESTIMATION IN CMOS VLSI CIRCUITS

2001 ◽  
Author(s):  
N. E. EVMORFOPOULOS ◽  
J. N. AVARITSIOTIS ◽  
G. I. STAMOULIS
Keyword(s):  
Power Estimation ◽  
Vlsi Circuits ◽  
Maximum Power ◽  
Cmos Vlsi

scholarly journals A New Statistical Method for Maximum Power Estimation in CMOS VLSI Circuits

2000 ◽  
Vol 22 (3) ◽  
pp. 215-233 ◽  
Author(s):  
N. E. Evmorfopoulos ◽  
J. N. Avaritsiotis
Keyword(s):  
Probability Distribution ◽  
Power Consumption ◽  
Extreme Value Theory ◽  
Power Estimation ◽  
Value Theory ◽  
Vlsi Circuits ◽  
Maximum Power ◽  
Instantaneous Power ◽  
Simplifying Assumptions ◽  
Cmos Vlsi

A method for maximum power estimation in CMOS VLSI circuits is proposed. The method is based on extreme value theory and allows for the calculation of the upper end point of the probability distribution which is followed by the instantaneous power drawn from the supply bus. The main features of the method are the relatively small and circuitin-dependent subset of input patterns required for accurate prediction of maximum power and its simulative nature which ensures that no over-simplifying assumptions are made. Application of the proposed method to eight distributions, which come close to the behavior of power consumption in VLSI circuits, proved its superior capabilities with respect to existing methods.

scholarly journals Machine Learning Based Power Estimation for CMOS VLSI Circuits

2021 ◽  
Keyword(s):  
Machine Learning ◽  
Power Estimation ◽  
Vlsi Circuits ◽  
Cmos Vlsi

Abstract The authors have requested that this preprint be withdrawn due to a need to make corrections.

scholarly journals Machine Learning Based Power Estimation for CMOS VLSI Circuits

2021 ◽  
Author(s):  
V. Govindaraj ◽  
B. Arunadevi
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Mean Square Error ◽  
Power Estimation ◽  
Vlsi Circuits ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Mean Square ◽  
Nsga Ii ◽  
Cmos Vlsi

Abstract Nowdays, machine learning (ML) algorithms are receiving massive attention in most of the engineering application since it has capability in complex systems modelling 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 multi-objective NSGA-II algorithm. It is inferred from the experimental results testing error varies from 1.4 percent to 6.8 percent 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.

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