| |  Online-Ressource |
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| Verfasst von: | Arnold, Elias [VerfasserIn]  |
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| | Böcherer, Georg [VerfasserIn] |
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| | Strasser, Florian [VerfasserIn] |
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| | Müller, Eric [VerfasserIn] |
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| | Spilger, Philipp [VerfasserIn] |
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| | Billaudelle, Sebastian [VerfasserIn] |
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| | Weis, Johannes [VerfasserIn] |
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| | Schemmel, Johannes [VerfasserIn] |
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| | Calabrò, Stefano [VerfasserIn] |
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| | Kuschnerov, Maxim [VerfasserIn] |
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| Titel: | Spiking neural network nonlinear demapping on neuromorphic hardware for IM/DD optical communication |
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| Verf.angabe: | Elias Arnold, Georg Böcherer, Florian Strasser, Eric Müller, Philipp Spilger, Sebastian Billaudelle, Johannes Weis, Johannes Schemmel, Stefano Calabrò, Maxim Kuschnerov |
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| E-Jahr: | 2023 |
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| Jahr: | 01 June 2023 |
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| Umfang: | 8 S. |
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| Fussnoten: | Veröffentlicht: 6. März 2023 ; Gesehen am 17.08.2023 |
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| Titel Quelle: | Enthalten in: Journal of lightwave technology |
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| Ort Quelle: | Washington, DC : Optica, 1983 |
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| Jahr Quelle: | 2023 |
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| Band/Heft Quelle: | 41(2023), 11 vom: Juni, Seite 3424-3431 |
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| ISSN Quelle: | 1558-2213 |
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| Abstract: | Neuromorphic computing implementing spiking neural networks (SNN) is a promising technology for reducing the footprint of optical transceivers, as required by the fast-paced growth of data center traffic. In this work, an SNN nonlinear demapper is designed and evaluated on a simulated intensity-modulation direct-detection link with chromatic dispersion. The SNN demapper is implemented in software and on the analog neuromorphic hardware system BrainScaleS-2 (BSS-2). For comparison, linear equalization (LE), Volterra nonlinear equalization (VNLE), and nonlinear demapping by an artificial neural network (ANN) implemented in software are considered. At a pre-forward error correction bit error rate of 2×10−3 , the software SNN outperforms LE by 1.5 dB, VNLE by 0.3 dB and the ANN by 0.5 dB. The hardware penalty of the SNN on BSS-2 is only 0.2 dB, i.e., also on hardware, the SNN performs better than all software implementations of the reference approaches. Hence, this work demonstrates that SNN demappers implemented on electrical analog hardware can realize powerful and accurate signal processing fulfilling the strict requirements of optical communications. |
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| DOI: | doi:10.1109/JLT.2023.3252819 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext: https://dx.doi.org/10.1109/JLT.2023.3252819 |
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| | DOI: https://doi.org/10.1109/JLT.2023.3252819 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Adaptive optics |
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| | Data centers |
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| | equalization |
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| | Equalizers |
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| | Hardware |
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| | intensity-modulation direct-detection |
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| | Neurons |
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| | Nonlinear optics |
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| | optical communication |
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| | Optical fiber dispersion |
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| | Software |
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| | spiking neural network |
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| K10plus-PPN: | 1856424235 |
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| Verknüpfungen: | → Zeitschrift |
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Spiking neural network nonlinear demapping on neuromorphic hardware for IM/DD optical communication / Arnold, Elias [VerfasserIn]; 01 June 2023 (Online-Ressource)
