- Titel
- Optical neural networks for low-latency and energy efficient applications in production
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:l189-qucosa2-943136
- Konferenz
- KI 2024 - 47th German Conference on Artificial Intelligence. Würzburg, 25.-27.09.2024
- Quellenangabe
- Workshop Proceedings AI in Production - 1
Herausgeber: Martin Krockert
Herausgeber: Torsten Munkelt
Erscheinungsort: Leipzig
Verlag: Hochschule für Technik, Wirtschaft und Kultur Leipzig
Erscheinungsjahr: 2024
Titel Schriftenreihe: AI in Production
Bandnummer Schriftenreihe: 1
Auflage: 1 - DOI
- https://doi.org/10.33968/2024.76
- Abstract (EN)
- Novel photonic compute hardware is a rising and promising technology wherever low-latency or energy efficient computation is required. Especially, optical neural networks (ONNs) aim to provide accelerators for artificial intelligence (AI) applications. In this work, we present a prototype of an optical matrix-vector multiplier. As the technology is still in its infancy, we motivate an outlook on future performances. Furthermore, we map our technology to the field of (automotive) production, where ONNs may be applied in the future. Hereby, we take into account the Profinet communication protocol, which is widely used by German car manufactures. This paper manifests a proposal for future applications of ONNs in production and its logistics.
- Freie Schlagwörter (EN)
- Optical Neural Networks, AI in Production, Automotive
- Herausgeber (Institution)
- Hochschule für Technik, Wirtschaft und Kultur Leipzig, Leipzig
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:l189-qucosa2-943136
- Veröffentlichungsdatum Qucosa
- 04.11.2024
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0