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Addressing limited weight resolution in a fully optical neuromorphic reservoir computing readout
Using optical hardware for neuromorphic computing has become more and more popular recently, due to its efficient high-speed data processing capabilities and low power consumption. However, there are still some remaining obstacles to realizing the vision of a completely optical neuromorphic computer...
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/PMC7862222/ https://www.ncbi.nlm.nih.gov/pubmed/33542496 http://dx.doi.org/10.1038/s41598-021-82720-4 |
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author | Ma, Chonghuai Laporte, Floris Dambre, Joni Bienstman, Peter |
author_facet | Ma, Chonghuai Laporte, Floris Dambre, Joni Bienstman, Peter |
author_sort | Ma, Chonghuai |
collection | PubMed |
description | Using optical hardware for neuromorphic computing has become more and more popular recently, due to its efficient high-speed data processing capabilities and low power consumption. However, there are still some remaining obstacles to realizing the vision of a completely optical neuromorphic computer. One of them is that, depending on the technology used, optical weighting elements may not share the same resolution as in the electrical domain. Moreover, noise of the weighting elements are important considerations as well. In this article, we investigate a new method for improving the performance of optical weighting components, even in the presence of noise and in the case of very low resolution. Our method utilizes an iterative training procedure and is able to select weight connections that are more robust to quantization and noise. As a result, even with only 8 to 32 levels of resolution, in noisy weighting environments, the method can outperform both nearest rounding low-resolution weighting and random rounding weighting by up to several orders of magnitude in terms of bit error rate and can deliver performance very close to full-resolution weighting elements. |
format | Online Article Text |
id | pubmed-7862222 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78622222021-02-05 Addressing limited weight resolution in a fully optical neuromorphic reservoir computing readout Ma, Chonghuai Laporte, Floris Dambre, Joni Bienstman, Peter Sci Rep Article Using optical hardware for neuromorphic computing has become more and more popular recently, due to its efficient high-speed data processing capabilities and low power consumption. However, there are still some remaining obstacles to realizing the vision of a completely optical neuromorphic computer. One of them is that, depending on the technology used, optical weighting elements may not share the same resolution as in the electrical domain. Moreover, noise of the weighting elements are important considerations as well. In this article, we investigate a new method for improving the performance of optical weighting components, even in the presence of noise and in the case of very low resolution. Our method utilizes an iterative training procedure and is able to select weight connections that are more robust to quantization and noise. As a result, even with only 8 to 32 levels of resolution, in noisy weighting environments, the method can outperform both nearest rounding low-resolution weighting and random rounding weighting by up to several orders of magnitude in terms of bit error rate and can deliver performance very close to full-resolution weighting elements. Nature Publishing Group UK 2021-02-04 /pmc/articles/PMC7862222/ /pubmed/33542496 http://dx.doi.org/10.1038/s41598-021-82720-4 Text en © The Author(s) 2021 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/. |
spellingShingle | Article Ma, Chonghuai Laporte, Floris Dambre, Joni Bienstman, Peter Addressing limited weight resolution in a fully optical neuromorphic reservoir computing readout |
title | Addressing limited weight resolution in a fully optical neuromorphic reservoir computing readout |
title_full | Addressing limited weight resolution in a fully optical neuromorphic reservoir computing readout |
title_fullStr | Addressing limited weight resolution in a fully optical neuromorphic reservoir computing readout |
title_full_unstemmed | Addressing limited weight resolution in a fully optical neuromorphic reservoir computing readout |
title_short | Addressing limited weight resolution in a fully optical neuromorphic reservoir computing readout |
title_sort | addressing limited weight resolution in a fully optical neuromorphic reservoir computing readout |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7862222/ https://www.ncbi.nlm.nih.gov/pubmed/33542496 http://dx.doi.org/10.1038/s41598-021-82720-4 |
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