Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloFirst steps towards the realization of a double layer perceptron based on organic memristive devices
Anno di pubblicazione2016
FormatoElettronico
Autore/iEmelyanov A. V.; Lapkin D. A.; Demin V. A.; Erokhin V. V.; Battistoni S.; Baldi G.; Dimonte A.; Korovin A. N.; Iannotta S.; Kashkarov P. K.; Kovalchuk M. V.
Affiliazioni autoriNational Research Centre "Kurchatov Institute", 123182 Moscow, Russia; Moscow Institute of Physics and Technology (State University), 141700 Dolgoprudny,Moscow Region, Russia; CNR-IMEM (National Research Council, Institute of Materials for Electronics and Magnetism) and University of Parma, Viale Usberti 7A, 42124 Parma, Italy; Physics Department, Lomonosov Moscow State University, GSP-1, Leninskie Gory, 119991 Moscow, Russia; Physics Department, Saint Petersburg State University, Saint Petersburg 199034, Russia
Autori CNR e affiliazioni
  • ALICE DIMONTE
  • SILVIA BATTISTONI
  • SALVATORE IANNOTTA
  • GIACOMO BALDI
  • VICTOR EROKHIN
Lingua/e
  • inglese
AbstractMemristors are widely considered as promising elements for the efficient implementation of synaptic weights in artificial neural networks (ANNs) since they are resistors that keep memory of their previous conductive state. Whereas demonstrations of simple neural networks (e.g., a single-layer perceptron) based on memristors already exist, the implementation of more complicated networks is more challenging and has yet to be reported. In this study, we demonstrate linearly nonseparable combinational logic classification (XOR logic task) using a network implemented with CMOS-based neurons and organic memrisitive devices that constitutes the first step toward the realization of a double layer perceptron. We also show numerically the ability of such network to solve a principally analogue task which cannot be realized by digital devices. The obtained results prove the possibility to create a multilayer ANN based on memristive devices that paves the way for designing a more complex network such as the double layer perceptron.
Lingua abstractinglese
Altro abstract-
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Pagine da111301-1
Pagine a111301-9
Pagine totali9
RivistaAIP advances
Attiva dal 2011
Editore: American Institute of Physics - Melville, NY
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 2158-3226
Titolo chiave: AIP advances
Titolo proprio: AIP advances
Titolo abbreviato: AIP adv.
Titolo alternativo: American Institute of Physics advances
Numero volume della rivista6
Fascicolo della rivista11
DOI10.1063/1.4966257
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-84994027721)
  • ISI Web of Science (WOS) (Codice:000392082600002)
Parole chiavelearning, Artifical Neural Network
Link (URL, URI)http://aip.scitation.org/doi/abs/10.1063/1.4966257
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Strutture CNR
  • IMEM — Istituto dei materiali per l'elettronica ed il magnetismo
Moduli/Attività/Sottoprogetti CNR-
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Allegati
First steps towards the realization of a double layer perceptron based on organic memristive devices
Descrizione: Versione finale pubblicata in pdf
Tipo documento: application/pdf