Open AccessQuantitative determination of fault tolerance of memristor-based artificial neural networks
Author(s) -
S.N. Danilin,
Sergey Shchanikov,
Ilya Bordanov,
A. D. Zuev
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1333/6/062027
Subject(s) - dependability , artificial neural network , memristor , fault tolerance , computer science , rationalization (economics) , artificial intelligence , reliability engineering , machine learning , engineering , distributed computing , electronic engineering , philosophy , epistemology
The authors have reviewed interpretations of the terms “dependability” and “fault-tolerance” in Russian and interstate standards. A new quantitative criterion of fault-tolerance of the memristors-based artificial neural networks is proposed and substantiated. The authors have also proposed and provided rationalization for a revised definition of fault-tolerance as a property of the memristors-based artificial neural networks, which most fully conforms to the new version of its quantitative criterion. An example of the application practice for the fault-tolerance criterion during the design stage of an artificial neural network of a test degree of complexity is given.