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Supervised neural networks with memristor binary synapses
Author(s) -
Secco Jacopo,
Poggio Mauro,
Corinto Fernando
Publication year - 2018
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.2429
Subject(s) - memristor , artificial neural network , computer science , artificial intelligence , neuromorphic engineering , set (abstract data type) , physical neural network , memistor , binary number , key (lock) , supervised learning , machine learning , types of artificial neural networks , time delay neural network , resistive random access memory , engineering , electronic engineering , mathematics , electrical engineering , voltage , arithmetic , computer security , programming language
Summary Memristors are emerging devices that promise the efficient implementation of synapses in artificial neural networks. Memristors have permitted the processing and analysis of a large amount of data in evolutionary learning artificial systems through signals that can be assimilated to human brain‐like neurotransmitters and synapses. In this manuscript, we present a memristor‐based neural network implementing the Stochastic Belief‐Propagation‐Inspired algorithm, an efficient supervised learning algorithm (which infers a classification rule from a set of labelled examples) suited for devices with very‐low‐precision synaptic weights. Synapses are represented by memristor devices described by the Generalized Boundary Condition Memristor model. We will thus demonstrate how to implement the key features of a machine learning algorithm in real‐world circuitry. Copyright © 2017 John Wiley & Sons, Ltd.