The thin line for optical neural networks towards broad practical relevance
Fuente:
arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1866910232325652480 |
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| author | Skalli, Anas Brunner, Daniel |
| author_facet | Skalli, Anas Brunner, Daniel |
| contents | Optical neural networks promise unmatched efficiency, bandwidth, and latency, critical benefits as demand for neural network hardware surges. However, their practical value for general-purpose acceleration or specialized applications must be proven under application-realistic conditions. We discuss recent insights and outline key research priorities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_18178 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The thin line for optical neural networks towards broad practical relevance Skalli, Anas Brunner, Daniel Optics Optical neural networks promise unmatched efficiency, bandwidth, and latency, critical benefits as demand for neural network hardware surges. However, their practical value for general-purpose acceleration or specialized applications must be proven under application-realistic conditions. We discuss recent insights and outline key research priorities. |
| title | The thin line for optical neural networks towards broad practical relevance |
| topic | Optics |
| url | https://arxiv.org/abs/2605.18178 |