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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2020
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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. |
format | Online Article Text |
id | pubmed-7449691 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
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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