Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloHardware elementary perceptron based on polyaniline memristive devices
Anno di pubblicazione2015
Formato
  • Elettronico
  • Cartaceo
Autore/iDemin V. A.; Erokhin V. V.; Emelyanov A. V.; Battistoni S.; Baldi G.; Iannotta S.; Kashkarov P. K.; Kovalchuk M. V.
Affiliazioni autoriNational Research Centre Kurchatov Institute, Moscow, 123182, Russian Federation; Moscow Institute of Physics and Technology (State University), Dolgoprudny, Moscow Region, 141700, Russian Federation; CNR-IMEM (National Research Council, Institute of Materials for Electronics and Magnetism), University of Parma, VialeUsberti 7A, Parma, 42124, Italy; Physics Department, Lomonosov Moscow State University, GSP-1, Leninskie Gory, Moscow, 119991, Russian Federation; Physics Department, Saint Petersburg State University, Saint Petersburg, 199034, Russian Federation
Autori CNR e affiliazioni
  • SALVATORE IANNOTTA
  • SILVIA BATTISTONI
  • GIACOMO BALDI
  • VICTOR EROKHIN
Lingua/e
  • inglese
AbstractElementary perceptron is an artificial neural network with a single layer of adaptive links and one output neuron that can solve simple linearly separable tasks such as invariant pattern recognition, linear approximation, prediction and others. We report on the hardware realization of the elementary perceptron with the use of polyaniline-based memristive devices as the analog link weights. An error correction algorithm was used to get the perceptron to learn the implementation of the NAND and NOR logic functions as examples of linearly separable tasks. The physical realization of an elementary perceptron demonstrates the ability to form the hardware-based neuromorphic networks with the use of organic memristive devices. The results provide a great promise toward new approaches for very compact, low-volatile and high-performance neurochips that could be made for a huge number of intellectual products and applications.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da16
Pagine a20
Pagine totali5
RivistaOrganic electronics (Print)
Attiva dal 2000
Editore: North-Holland : - Amsterdam
Paese di pubblicazione: Paesi Bassi
Lingua: inglese
ISSN: 1566-1199
Titolo chiave: Organic electronics (Print)
Titolo proprio: Organic electronics. (Print)
Numero volume della rivista25
Fascicolo della rivista-
DOI10.1016/j.orgel.2015.06.015
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000358603600004)
  • Scopus (Codice:2-s2.0-84930936504)
Parole chiaveMemristor, Perceptron, Pattern classification, Machine learning, Polyaniline, Neuromorphic computing
Link (URL, URI)https://www.sciencedirect.com/science/article/pii/S1566119915002633
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IMEM — Istituto dei materiali per l'elettronica ed il magnetismo
Moduli/Attività/Sottoprogetti CNR
  • MD.P06.037.001 : Crescita di materiali, funzionalizzazioni e dispositivi da precursori molecolari, inorganici e cluster
Progetti Europei-
Allegati
Hardware elementary perceptron based on polyaniline memristive devices (documento privato )
Descrizione: Versione finale pubblicata in pdf
Tipo documento: application/pdf