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Nanoparticle-based computing architecture for nanoparticle neural networks

The lack of a scalable nanoparticle-based computing architecture severely limits the potential and use of nanoparticles for manipulating and processing information with molecular computing schemes. Inspired by the von Neumann architecture (VNA), in which multiple programs can be operated without res...

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Autores principales: Kim, Sungi, Kim, Namjun, Seo, Jinyoung, Park, Jeong-Eun, Song, Eun Ho, Choi, So Young, Kim, Ji Eun, Cha, Seungsang, Park, Ha H., Nam, Jwa-Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7449691/
https://www.ncbi.nlm.nih.gov/pubmed/32923638
http://dx.doi.org/10.1126/sciadv.abb3348
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author Kim, Sungi
Kim, Namjun
Seo, Jinyoung
Park, Jeong-Eun
Song, Eun Ho
Choi, So Young
Kim, Ji Eun
Cha, Seungsang
Park, Ha H.
Nam, Jwa-Min
author_facet Kim, Sungi
Kim, Namjun
Seo, Jinyoung
Park, Jeong-Eun
Song, Eun Ho
Choi, So Young
Kim, Ji Eun
Cha, Seungsang
Park, Ha H.
Nam, Jwa-Min
author_sort Kim, Sungi
collection PubMed
description The lack of a scalable nanoparticle-based computing architecture severely limits the potential and use of nanoparticles for manipulating and processing information with molecular computing schemes. Inspired by the von Neumann architecture (VNA), in which multiple programs can be operated without restructuring the computer, we realized the nanoparticle-based VNA (NVNA) on a lipid chip for multiple executions of arbitrary molecular logic operations in the single chip without refabrication. In this system, nanoparticles on a lipid chip function as the hardware that features memory, processors, and output units, and DNA strands are used as the software to provide molecular instructions for the facile programming of logic circuits. NVNA enables a group of nanoparticles to form a feed-forward neural network, a perceptron, which implements functionally complete Boolean logic operations, and provides a programmable, resettable, scalable computing architecture and circuit board to form nanoparticle neural networks and make logical decisions.
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spelling pubmed-74496912020-09-11 Nanoparticle-based computing architecture for nanoparticle neural networks Kim, Sungi Kim, Namjun Seo, Jinyoung Park, Jeong-Eun Song, Eun Ho Choi, So Young Kim, Ji Eun Cha, Seungsang Park, Ha H. Nam, Jwa-Min Sci Adv Research Articles The lack of a scalable nanoparticle-based computing architecture severely limits the potential and use of nanoparticles for manipulating and processing information with molecular computing schemes. Inspired by the von Neumann architecture (VNA), in which multiple programs can be operated without restructuring the computer, we realized the nanoparticle-based VNA (NVNA) on a lipid chip for multiple executions of arbitrary molecular logic operations in the single chip without refabrication. In this system, nanoparticles on a lipid chip function as the hardware that features memory, processors, and output units, and DNA strands are used as the software to provide molecular instructions for the facile programming of logic circuits. NVNA enables a group of nanoparticles to form a feed-forward neural network, a perceptron, which implements functionally complete Boolean logic operations, and provides a programmable, resettable, scalable computing architecture and circuit board to form nanoparticle neural networks and make logical decisions. American Association for the Advancement of Science 2020-08-26 /pmc/articles/PMC7449691/ /pubmed/32923638 http://dx.doi.org/10.1126/sciadv.abb3348 Text en Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/ https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
spellingShingle Research Articles
Kim, Sungi
Kim, Namjun
Seo, Jinyoung
Park, Jeong-Eun
Song, Eun Ho
Choi, So Young
Kim, Ji Eun
Cha, Seungsang
Park, Ha H.
Nam, Jwa-Min
Nanoparticle-based computing architecture for nanoparticle neural networks
title Nanoparticle-based computing architecture for nanoparticle neural networks
title_full Nanoparticle-based computing architecture for nanoparticle neural networks
title_fullStr Nanoparticle-based computing architecture for nanoparticle neural networks
title_full_unstemmed Nanoparticle-based computing architecture for nanoparticle neural networks
title_short Nanoparticle-based computing architecture for nanoparticle neural networks
title_sort nanoparticle-based computing architecture for nanoparticle neural networks
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7449691/
https://www.ncbi.nlm.nih.gov/pubmed/32923638
http://dx.doi.org/10.1126/sciadv.abb3348
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