Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Ma, Chonghuai, Laporte, Floris, Dambre, Joni, Bienstman, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
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
_version_ 1783647242412883968
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
work_keys_str_mv AT machonghuai addressinglimitedweightresolutioninafullyopticalneuromorphicreservoircomputingreadout
AT laportefloris addressinglimitedweightresolutioninafullyopticalneuromorphicreservoircomputingreadout
AT dambrejoni addressinglimitedweightresolutioninafullyopticalneuromorphicreservoircomputingreadout
AT bienstmanpeter addressinglimitedweightresolutioninafullyopticalneuromorphicreservoircomputingreadout