Statistical Analysis of the Efficiency of an Integrated Voltage Regulator by means of a Machine Learning Model Coupled with Kriging Regression

Trinchero, R.; Larbi, M.; Swaminathan, M.; Canavero, F. G.

doi:10.1109/SaPIW.2019.8781659

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This paper presents a preliminary version of a probabilistic model for the uncertainty quantification of complex electronic systems resulting from the combination of the least-squares support vector machine (LS-SVM) and the Gaussian process (GP) regression. The proposed model, trained with a limited set of training pairs provided by a set of full-wave expensive simulations, is adopted for the prediction of the efficiency of an integrated voltage regulator (IVR) with 8 uniformly distributed random parameters. The accuracy and the feasibility of the proposed model have been investigated by comparing the model predictions and its confidence intervals with the results of a Monte Carlo (MC) full-wave simulation of the device.

Titolo:	Statistical Analysis of the Efficiency of an Integrated Voltage Regulator by means of a Machine Learning Model Coupled with Kriging Regression
Autori:	TRINCHERO, RICCARDO LARBI, MOURAD SWAMINATHAN, MADHAVAN CANAVERO, Flavio
Data di pubblicazione:	2019
Abstract:	This paper presents a preliminary version of a probabilistic model for the uncertainty quantifica...tion of complex electronic systems resulting from the combination of the least-squares support vector machine (LS-SVM) and the Gaussian process (GP) regression. The proposed model, trained with a limited set of training pairs provided by a set of full-wave expensive simulations, is adopted for the prediction of the efficiency of an integrated voltage regulator (IVR) with 8 uniformly distributed random parameters. The accuracy and the feasibility of the proposed model have been investigated by comparing the model predictions and its confidence intervals with the results of a Monte Carlo (MC) full-wave simulation of the device.
ISBN:	978-1-5386-8342-2
Appare nelle tipologie:	4.1 Contributo in Atti di convegno

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http://hdl.handle.net/11583/2768138

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