Web of Science: 2 cites, Scopus: 1 cites, Google Scholar: cites
Assessment and improvement of the pattern recognition performance of memdiode-based cross-point arrays with randomly distributed stuck-at-faults
Aguirre, Fernando Leonel (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)
Pazos, Sebastián M. (Consejo Nacional de Investigaciones Científicas y Técnicas)
Palumbo, Félix (Consejo Nacional de Investigaciones Científicas y Técnicas)
Morell Pérez, Antoni (Universitat Autònoma de Barcelona. Departament de Telecomunicació i Enginyeria de Sistemes)
Suñé, Jordi, 1963- (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)
Miranda, Enrique (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)

Data: 2021
Resum: In this work, the effect of randomly distributed stuck-at faults (SAFs) in memristive crosspoint array (CPA)-based single and multi-layer perceptrons (SLPs and MLPs, respectively) intended for pattern recognition tasks is investigated by means of realistic SPICE simulations. The quasi-static memdiode model (QMM) is considered here for the modelling of the synaptic weights implemented with memristors. Following the standard memristive approach, the QMM comprises two coupled equations, one for the electron transport based on the double-diode equation with a single series resistance and a second equation for the internal memory state of the device based on the so-called logistic hysteron. By modifying the state parameter in the current-voltage characteristic, SAFs of different severeness are simulated and the final outcome is analysed. Supervised ex-situ training and two well-known image datasets involving hand-written digits and human faces are employed to assess the inference accuracy of the SLP as a function of the faulty device ratio. The roles played by the memristor's electrical parameters, line resistance, mapping strategy, image pixelation, and fault type (stuck-at-ON or stuck-at-OFF) on the CPA performance are statistically analysed following a Monte-Carlo approach. Three different re-mapping schemes to help mitigate the effect of the SAFs in the SLP inference phase are thoroughly investigated.
Ajuts: Agencia Estatal de Investigación TEC2017-84321-C4-4-R
Drets: 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. Creative Commons
Llengua: Anglès
Document: Article ; recerca ; Versió publicada
Matèria: Stuck-at fault ; RRAM ; Pattern recognition ; Memristor ; QMM ; Neural network ; Neuromorphics
Publicat a: Electronics, Vol. 10, Issue 19 (October 2021) , art. 2427, ISSN 2079-9292

DOI: 10.3390/electronics10192427

24 p, 21.3 MB

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