Numerical simulation and experimental study of a hardware pulse neural network with memristor synapses

About the authors:

Alexander N. Busygin, Postgraduate Student, Department of Applied and Technical Physics, Reseacher Laboratory Assistant, REC “Nanotechnology”, University of Tyumen; eLibrary AuthorID, ScopusID, a.n.busygin@utmn.ru; ORCID: 0000-0002-3439-8067

Andrey N. Bobylev, Head of the Laboratory of Electronic and Probe Microscopy. REC “Nanotechnology”, University of Tyumen; eLibrary AuthorID, ScopusID, andreaubobylev@gmail.com; ORCID: 0000-0001-5488-8736

Alexey A. Gubin, Postgraduate Student, Department of Applied and Technical Physics, Engineer, REC “Nanotechnology”, University of Tyumen; a.a.gubin@utmn.ru

Alexander D. Pisarev, Cand. Sci. (Tech.), Associate Professor, Department of Applied and Technical Physics, Head of Laboratory of Beam-Plasma Technologies, SEC “Nanotechnologies”, University of Tyumen; eLibrary AuthorID, ORCID, Scopus AuthorID, spcb.doc@gmail.com

Sergey Yu. Udovichenko, Dr. Sci. (Phys.-Math.), Professor of Department of Applied and Technical Physics; Head of REC “Nanotechnology”, University of Tyumen; eLibrary AuthorID, ResearcherID, ScopusID, udotgu@mail.ru

Abstract:

This article presents the results of a numerical simulation and an experimental study of the electrical circuit of a hardware spiking perceptron based on a memristor-diode crossbar. That has required developing and manufacturing a measuring bench, the electrical circuit of which consists of a hardware perceptron circuit and an input peripheral electrical circuit to implement the activation functions of the neurons and ensure the operation of the memory matrix in a spiking mode. The authors have performed a study of the operation of the hardware spiking neural network with memristor synapses in the form of a memory matrix in the mode of a single-layer perceptron synapses. The perceptron can be considered as the first layer of a biomorphic neural network that performs primary processing of incoming information in a biomorphic neuroprocessor. The obtained experimental and simulation learning curves show the expected increase in the proportion of correct classifications with an increase in the number of training epochs. The authors demonstrate generating a new association during retraining caused by the presence of new input information. Comparison of the results of modeling and an experiment on training a small neural network with a small crossbar will allow creating adequate models of hardware neural networks with a large memristor-diode crossbar. The arrival of new unknown information at the input of the hardware spiking neural network can be related with the generation of new associations in the biomorphic neuroprocessor. With further improvement of the neural network, this information will be comprehended and, therefore, will allow the transition from weak to strong artificial intelligence.

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