Detecting hard synapses faults in artificial neural networks

This paper presents the concepts of detecting hard faults in artificial neural network synapses using the modification of the neural network settings. The core of this work is based on weights values modification and inserting the chosen testing data when comparing the neural network output to the k...

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Bibliographic Details
Published in2019 IEEE Latin American Test Symposium (LATS) pp. 1 - 6
Main Authors Krcma, Martin, Kotasek, Zdenek, Lojda, Jakub
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2019
Subjects
Biological neural networks
Hardware
Neurons
Redundancy
Synapses
Task analysis
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Summary:This paper presents the concepts of detecting hard faults in artificial neural network synapses using the modification of the neural network settings. The core of this work is based on weights values modification and inserting the chosen testing data when comparing the neural network output to the known valid results. The paper also discusses the problem of neural networks output saturation and provides experiments regarding an influence of the neural network settings to the problem.
DOI:10.1109/LATW.2019.8704637