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| Verfasst von: | Varri, Akhil [VerfasserIn]  |
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| | Brückerhoff-Plückelmann, Frank [VerfasserIn] |
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| | Dijkstra, Jelle [VerfasserIn] |
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| | Wendland, Daniel [VerfasserIn] |
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| | Bankwitz, Julian Rasmus [VerfasserIn] |
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| | Agnihotri, Apoorv [VerfasserIn] |
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| | Pernice, Wolfram [VerfasserIn] |
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| Titel: | Noise-resilient photonic analog neural networks |
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| Verf.angabe: | Akhil Varri, Frank Brückerhoff-Plückelmann, Jelle Dijkstra, Daniel Wendland, Rasmus Bankwitz, student member, IEEE, Apoorv Agnihotri, and Wolfram H.P. Pernice |
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| E-Jahr: | 2024 |
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| Jahr: | 15 November 2024 |
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| Umfang: | 8 S. |
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| Illustrationen: | Illustrationen |
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| Fussnoten: | Online verfügbar: 25. Juli 2024 ; Gesehen am 15.05.2025 |
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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: | 2024 |
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| Band/Heft Quelle: | 42(2024), 22, Seite 7969-7976 |
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| ISSN Quelle: | 1558-2213 |
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| Abstract: | The explosion of generative artificial intelligence (AI) has led to an unprecedented demand for AI accelerators. Photonic computing holds promise in this direction, offering speedups in bandwidth and latency. However, photonic integrated circuits (PICs) and their periphery input/output (I/O) components tend to be noisy due to the nature of analog computing. This can lead to accuracy degradation if not accounted for properly. In this paper, we characterize the typical noise levels present in photonic hardware accelerators for deep neural networks (DNNs). We explore several techniques including knowledge distillation, stability training, and standard Gaussian noise injection to improve the robustness of photonic DNNs. We validate our methods by training a Resnet model on the CIFAR-10 dataset and comparing the simulated test accuracy with different noise levels and image distortions. The robust training techniques discussed in this paper combined with the noise analysis of PICs provide a blueprint for robust photonic AI inference accelerators. |
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| DOI: | doi:10.1109/JLT.2024.3433454 |
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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://doi.org/10.1109/JLT.2024.3433454 |
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| | Volltext: https://ieeexplore.ieee.org/document/10609496 |
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| | DOI: https://doi.org/10.1109/JLT.2024.3433454 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Circuits |
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| | Electro-absorption modulators |
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| | field programmable gate array (FPGA) |
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| | Neural networks |
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| | Noise |
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| | Photonic integrated circuits |
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| | photonic neural networks |
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| | Photonics |
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| | robust deep neural networks |
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| | Signal to noise ratio |
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| | silicon photonics |
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| | Training |
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| K10plus-PPN: | 1925776131 |
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| Verknüpfungen: | → Zeitschrift |
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Noise-resilient photonic analog neural networks / Varri, Akhil [VerfasserIn]; 15 November 2024 (Online-Ressource)
