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Flexible Neural Network Realized by the Probabilistic SiO (x) Memristive Synaptic Array for Energy‐Efficient Image Learning
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 ste...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9009121/ https://www.ncbi.nlm.nih.gov/pubmed/35170246 http://dx.doi.org/10.1002/advs.202104773 |
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author | Choi, Sanghyeon Jang, Jingon Kim, Min Seob Kim, Nam Dong Kwag, Jeehyun Wang, Gunuk |
author_facet | Choi, Sanghyeon Jang, Jingon Kim, Min Seob Kim, Nam Dong Kwag, Jeehyun Wang, Gunuk |
author_sort | Choi, Sanghyeon |
collection | PubMed |
description | 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 (x) memristive synaptic barristor utilizing the Si/graphene heterojunction is designed and fabricated. Controllable stochastic switching dynamics in this array are achieved via various input voltage pulse schemes. In particular, the threshold tunability via electrostatic gating enables the efficient in situ alteration of the probabilistic switching activation (P ( Act )) from 0 to 1.0, and can even modulate the degree of the P ( Act ) change. A drop‐connected algorithm based on the P ( Act ) is constructed and used to successfully classify the shapes of several fashion items. The suggested approach can decrease the learning energy by up to ≈2,116 times relative to that of the conventional all‐to‐all connected network while exhibiting a high recognition accuracy of ≈93 %. |
format | Online Article Text |
id | pubmed-9009121 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90091212022-04-15 Flexible Neural Network Realized by the Probabilistic SiO (x) Memristive Synaptic Array for Energy‐Efficient Image Learning Choi, Sanghyeon Jang, Jingon Kim, Min Seob Kim, Nam Dong Kwag, Jeehyun Wang, Gunuk Adv Sci (Weinh) Research Articles 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 (x) memristive synaptic barristor utilizing the Si/graphene heterojunction is designed and fabricated. Controllable stochastic switching dynamics in this array are achieved via various input voltage pulse schemes. In particular, the threshold tunability via electrostatic gating enables the efficient in situ alteration of the probabilistic switching activation (P ( Act )) from 0 to 1.0, and can even modulate the degree of the P ( Act ) change. A drop‐connected algorithm based on the P ( Act ) is constructed and used to successfully classify the shapes of several fashion items. The suggested approach can decrease the learning energy by up to ≈2,116 times relative to that of the conventional all‐to‐all connected network while exhibiting a high recognition accuracy of ≈93 %. John Wiley and Sons Inc. 2022-02-16 /pmc/articles/PMC9009121/ /pubmed/35170246 http://dx.doi.org/10.1002/advs.202104773 Text en © 2022 The Authors. Advanced Science published by Wiley‐VCH GmbH https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Choi, Sanghyeon Jang, Jingon Kim, Min Seob Kim, Nam Dong Kwag, Jeehyun Wang, Gunuk Flexible Neural Network Realized by the Probabilistic SiO (x) Memristive Synaptic Array for Energy‐Efficient Image Learning |
title | Flexible Neural Network Realized by the Probabilistic SiO
(x)
Memristive Synaptic Array for Energy‐Efficient Image Learning |
title_full | Flexible Neural Network Realized by the Probabilistic SiO
(x)
Memristive Synaptic Array for Energy‐Efficient Image Learning |
title_fullStr | Flexible Neural Network Realized by the Probabilistic SiO
(x)
Memristive Synaptic Array for Energy‐Efficient Image Learning |
title_full_unstemmed | Flexible Neural Network Realized by the Probabilistic SiO
(x)
Memristive Synaptic Array for Energy‐Efficient Image Learning |
title_short | Flexible Neural Network Realized by the Probabilistic SiO
(x)
Memristive Synaptic Array for Energy‐Efficient Image Learning |
title_sort | flexible neural network realized by the probabilistic sio
(x)
memristive synaptic array for energy‐efficient image learning |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9009121/ https://www.ncbi.nlm.nih.gov/pubmed/35170246 http://dx.doi.org/10.1002/advs.202104773 |
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