Application of the Quasi-Static Memdiode Model in Cross-Point Arrays for Large Dataset Pattern Recognition
Aguirre, Fernando Leonel (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)
Pazos, Sebastián Matías (Consejo Nacional de Investigaciones Científicas y Técnicas)
Palumbo, Félix (Consejo Nacional de Investigaciones Científicas y Técnicas)
Suñé, Jordi 1963- (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)
Miranda, Enrique (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)

Date:	2020
Abstract:	We investigate the use and performance of the quasi-static memdiode model (QMM) when incorporated into large cross-point arrays intended for pattern classification tasks. Following Chua's memristive devices theory, the QMM comprises two equations, one equation for the electron transport based on the double-diode circuit with single series resistance and a second equation for the internal memory state of the device based on the so-called logistic hysteron or memory map. Ex-situ trained memdiodes with different MNIST-like databases are used to establish the synaptic weights linking the top and bottom wire networks. The role played by the memdiode electrical parameters, wire resistance and capacitance values, image pixelation, connection schemes, signal-to-noise ratio and device-to-device variability in the classification effectiveness are investigated. The confusion matrix is used to benchmark the system performance metrics. We show that the simplicity, accuracy and robustness of the memdiode model makes it a suitable candidate for the realistic simulation of large-scale neural networks with non-idealities.
Grants:	Agencia Estatal de Investigación TEC2017-84321-C4-4-R European Commission 783176
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	RRAM ; Resistive switching ; Cross-point ; Memory ; Memristor ; Neuromorphic ; Pattern recognition
Published in:	IEEE Access, Vol. 8 (November 2020) , p. 202174-202193, ISSN 2169-3536

DOI: 10.1109/ACCESS.2020.3035638

20 p, 4.7 MB

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