Using Neural Network Based Active Learning for Modelling Integrated Circuits Behavior

This paper presents an efficient sampling active learning algorithm that can be used for modelling integrated circuits behavior by using neural networks regressions. This sampling scheme uses the variance of multiple regression models in order to iteratively select a minimal training sample set that...

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Bibliographic Details
Published in2023 International Symposium on Signals, Circuits and Systems (ISSCS) pp. 1 - 4
Main Authors Grosu, Vasile, Goras, Liviu, David, Emilian, Pelz, Georg
Format Conference Proceeding
LanguageEnglish
Published IEEE 13.07.2023
Subjects
active learning
Behavioral sciences
Costs
integrated circuit design
Integrated circuit modeling
low drop-out voltage regulator
metamodels
neural network regression
Regulators
Sampling methods
Training
Uncertainty
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Summary:This paper presents an efficient sampling active learning algorithm that can be used for modelling integrated circuits behavior by using neural networks regressions. This sampling scheme uses the variance of multiple regression models in order to iteratively select a minimal training sample set that leads to better accuracy regression models when compared to classical fixed sampling methods. We validate the efficiency of this approach by both using a synthetic function and also a simulated Low drop-out voltage regulator.
DOI:10.1109/ISSCS58449.2023.10190907