Neuromorphic computing aims at the realization of intelligent systems able to process information similarly to our brain. Brain-inspired computing paradigms have been implemented in crossbar arrays of memristive devices; however, this approach does not emulate the topology and the emergent behaviour of biological neuronal circuits, where the principle of self-organization regulates both structure and function. Here, we report on in materia reservoir computing in a fully memristive architecture based on self-organized nanowire networks. Thanks to the functional synaptic connectivity with nonlinear dynamics and fading memory properties, the designless nanowire complex network acts as a network-wide physical reservoir able to map spatio-temporal inputs into a feature space that can be analysed by a memristive resistive switching memory read-out layer. Computing capabilities, including recognition of spatio-temporal patterns and time-series prediction, show that the emergent memristive behaviour of nanowire networks allows in materia implementation of brain-inspired computing paradigms characterized by a reduced training cost.A network of self-organized nanowires combined with a memristive read-out layer is used to demonstrate a hardware implementation of reservoir computing for recognition of spatio-temporal patterns and time-series prediction.

In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks / Milano, Gianluca; Pedretti, Giacomo; Montano, Kevin; Ricci, Saverio; Hashemkhani, Shahin; Boarino, Luca; Ielmini, Daniele; Ricciardi, Carlo. - In: NATURE MATERIALS. - ISSN 1476-1122. - 21:2(2022), pp. 195-202. [10.1038/s41563-021-01099-9]

In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks

Milano, Gianluca
;
Boarino, Luca;
2022

Abstract

Neuromorphic computing aims at the realization of intelligent systems able to process information similarly to our brain. Brain-inspired computing paradigms have been implemented in crossbar arrays of memristive devices; however, this approach does not emulate the topology and the emergent behaviour of biological neuronal circuits, where the principle of self-organization regulates both structure and function. Here, we report on in materia reservoir computing in a fully memristive architecture based on self-organized nanowire networks. Thanks to the functional synaptic connectivity with nonlinear dynamics and fading memory properties, the designless nanowire complex network acts as a network-wide physical reservoir able to map spatio-temporal inputs into a feature space that can be analysed by a memristive resistive switching memory read-out layer. Computing capabilities, including recognition of spatio-temporal patterns and time-series prediction, show that the emergent memristive behaviour of nanowire networks allows in materia implementation of brain-inspired computing paradigms characterized by a reduced training cost.A network of self-organized nanowires combined with a memristive read-out layer is used to demonstrate a hardware implementation of reservoir computing for recognition of spatio-temporal patterns and time-series prediction.
File in questo prodotto:
File Dimensione Formato  
s41563-021-01099-9.pdf

non disponibili

Tipologia: Versione editoriale
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 1.9 MB
Formato Adobe PDF
1.9 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Manuscript_revised_rev_2_v2.pdf

accesso aperto

Tipologia: Documento in Post-print
Licenza: Pubblico - Tutti i diritti riservati
Dimensione 4.82 MB
Formato Adobe PDF
4.82 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11696/73114
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 30
  • ???jsp.display-item.citation.isi??? 33
social impact