Flexible Neural Network Realized by the Probabilistic SiO
barristor
drop-connected network
neuromorphic computing
probabilistic synapse
silicon
silicon oxide
Journal
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
ISSN: 2198-3844
Titre abrégé: Adv Sci (Weinh)
Pays: Germany
ID NLM: 101664569
Informations de publication
Date de publication:
04 2022
04 2022
Historique:
revised:
31
12
2021
received:
24
10
2021
pubmed:
17
2
2022
medline:
16
4
2022
entrez:
16
2
2022
Statut:
ppublish
Résumé
The human brain's neural networks are sparsely connected via tunable and probabilistic synapses, which may be essential for performing energy-efficient cognitive and intellectual functions. In this sense, the implementation of a flexible neural network with probabilistic synapses is a first step toward realizing the ultimate energy-efficient computing framework. Here, inspired by the efficient threshold-tunable and probabilistic rod-to-rod bipolar synapses in the human visual system, a 16 × 16 crossbar array comprising the vertical form of gate-tunable probabilistic SiO
Identifiants
pubmed: 35170246
doi: 10.1002/advs.202104773
pmc: PMC9009121
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2104773Subventions
Organisme : National Research Foundation of Korea
Organisme : NRF
ID : 2019R1A2C2003704
Organisme : NRF
ID : 2020M3F3A2A03082825
Organisme : NRF
ID : 2022M3H4A1A01009526
Organisme : NRF
ID : 2020R1A2C201423512
Organisme : KU-KIST Graduate School Program of Korea University
Organisme : Korea University Future Research
Informations de copyright
© 2022 The Authors. Advanced Science published by Wiley-VCH GmbH.
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