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Realization and training of an inverter-based printed neuromorphic computing system
Emerging applications in soft robotics, wearables, smart consumer products or IoT-devices benefit from soft materials, flexible substrates in conjunction with electronic functionality. Due to high production costs and conformity restrictions, rigid silicon technologies do not meet application requir...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8099883/ https://www.ncbi.nlm.nih.gov/pubmed/33953238 http://dx.doi.org/10.1038/s41598-021-88396-0 |
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author | Weller, Dennis D. Hefenbrock, Michael Beigl, Michael Aghassi-Hagmann, Jasmin Tahoori, Mehdi B. |
author_facet | Weller, Dennis D. Hefenbrock, Michael Beigl, Michael Aghassi-Hagmann, Jasmin Tahoori, Mehdi B. |
author_sort | Weller, Dennis D. |
collection | PubMed |
description | Emerging applications in soft robotics, wearables, smart consumer products or IoT-devices benefit from soft materials, flexible substrates in conjunction with electronic functionality. Due to high production costs and conformity restrictions, rigid silicon technologies do not meet application requirements in these new domains. However, whenever signal processing becomes too comprehensive, silicon technology must be used for the high-performance computing unit. At the same time, designing everything in flexible or printed electronics using conventional digital logic is not feasible yet due to the limitations of printed technologies in terms of performance, power and integration density. We propose to rather use the strengths of neuromorphic computing architectures consisting in their homogeneous topologies, few building blocks and analog signal processing to be mapped to an inkjet-printed hardware architecture. It has remained a challenge to demonstrate non-linear elements besides weighted aggregation. We demonstrate in this work printed hardware building blocks such as inverter-based comprehensive weight representation and resistive crossbars as well as printed transistor-based activation functions. In addition, we present a learning algorithm developed to train the proposed printed NCS architecture based on specific requirements and constraints of the technology. |
format | Online Article Text |
id | pubmed-8099883 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-80998832021-05-07 Realization and training of an inverter-based printed neuromorphic computing system Weller, Dennis D. Hefenbrock, Michael Beigl, Michael Aghassi-Hagmann, Jasmin Tahoori, Mehdi B. Sci Rep Article Emerging applications in soft robotics, wearables, smart consumer products or IoT-devices benefit from soft materials, flexible substrates in conjunction with electronic functionality. Due to high production costs and conformity restrictions, rigid silicon technologies do not meet application requirements in these new domains. However, whenever signal processing becomes too comprehensive, silicon technology must be used for the high-performance computing unit. At the same time, designing everything in flexible or printed electronics using conventional digital logic is not feasible yet due to the limitations of printed technologies in terms of performance, power and integration density. We propose to rather use the strengths of neuromorphic computing architectures consisting in their homogeneous topologies, few building blocks and analog signal processing to be mapped to an inkjet-printed hardware architecture. It has remained a challenge to demonstrate non-linear elements besides weighted aggregation. We demonstrate in this work printed hardware building blocks such as inverter-based comprehensive weight representation and resistive crossbars as well as printed transistor-based activation functions. In addition, we present a learning algorithm developed to train the proposed printed NCS architecture based on specific requirements and constraints of the technology. Nature Publishing Group UK 2021-05-05 /pmc/articles/PMC8099883/ /pubmed/33953238 http://dx.doi.org/10.1038/s41598-021-88396-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Weller, Dennis D. Hefenbrock, Michael Beigl, Michael Aghassi-Hagmann, Jasmin Tahoori, Mehdi B. Realization and training of an inverter-based printed neuromorphic computing system |
title | Realization and training of an inverter-based printed neuromorphic computing system |
title_full | Realization and training of an inverter-based printed neuromorphic computing system |
title_fullStr | Realization and training of an inverter-based printed neuromorphic computing system |
title_full_unstemmed | Realization and training of an inverter-based printed neuromorphic computing system |
title_short | Realization and training of an inverter-based printed neuromorphic computing system |
title_sort | realization and training of an inverter-based printed neuromorphic computing system |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8099883/ https://www.ncbi.nlm.nih.gov/pubmed/33953238 http://dx.doi.org/10.1038/s41598-021-88396-0 |
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