The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Reinforcement learning in a large-scale photonic recurrent neural network
Open accessAbstract
Photonic Neural Network implementations have been gaining considerable attention as a potentially disruptive future technology. Demonstrating learning in large scale neural networks is essential to establish photonic machine learning substrates as viable information processing systems. Realizing photonic Neural Networks with numerous nonlinear nodes in a fully parallel and efficient learning hardware was lacking so far. We demonstrate a network of up to 2500 diffractively coupled photonic nodes, forming a large scale Recurrent Neural Network. Using a Digital Micro Mirror Device, we realize reinforcement learning. Our scheme is fully parallel, and the passive weights maximize energy efficiency and bandwidth. The computational output efficiently converges and we achieve very good performance.
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Self-citationParallel photonic information processing at gigabyte per second data rates using transient states
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Self-citationOptoelectronic Reservoir Computing
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Recent advances in physical reservoir computing: A review
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Photonics for artificial intelligence and neuromorphic computing
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An on-chip photonic deep neural network for image classification
Ashtiani, F., Geers, A. J., & Aflatouni, F. (2022) · Nature · doi:10.1038/s41586-022-04714-0 · Cited by 699
An optical neural chip for implementing complex-valued neural network
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A programmable diffractive deep neural network based on a digital-coding metasurface array
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Metadata
- Authors
- Bueno, J., Maktoobi, S., Froehly, L., Fischer, I., Jacquot, M., Larger, L., & Brunner, D.
- Venue
- Optica
- Publisher
- Optica Publishing Group
- Published
- 2018-06-20
- Volume
- 5
- Issue
- 6
- Pages
- 756
- ISSN
- 2334-2536 (ISSN-L)
- OpenAlex
- W2770947648
- Cited by
- 403
- References
- 16
- FWCI
- 23.48
- Type
- journal article
- Language
- en
- Open access
- gold
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