The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
All-optical reservoir computing
Open accessAbstract
Reservoir Computing is a novel computing paradigm that uses a nonlinear recurrent dynamical system to carry out information processing. Recent electronic and optoelectronic Reservoir Computers based on an architecture with a single nonlinear node and a delay loop have shown performance on standardized tasks comparable to state-of-the-art digital implementations. Here we report an all-optical implementation of a Reservoir Computer, made of off-the-shelf components for optical telecommunications. It uses the saturation of a semiconductor optical amplifier as nonlinearity. The present work shows that, within the Reservoir Computing paradigm, all-optical computing with state-of-the-art performance is possible.
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Cites
Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication
Jaeger, H., & Haas, H. (2004) · Science · doi:10.1126/science.1091277 · Cited by 3,811
Reservoir computing approaches to recurrent neural network training
Lukoševičius, M., & Jaeger, H. (2009) · Computer Science Review · doi:10.1016/j.cosrev.2009.03.005 · Cited by 2,956
Optimization and applications of echo state networks with leaky- integrator neurons
Jaeger, H., Lukoševičius, M., Popovici, D., & Siewert, U. (2007) · Neural Networks · doi:10.1016/j.neunet.2007.04.016 · Cited by 948
Cited by
Recent advances in physical reservoir computing: A review
Tanaka, G., Yamane, T., Héroux, J. B., Nakane, R., Kanazawa, N., Takeda, S., Numata, H., Nakano, D., & Hirose, A. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.03.005 · Cited by 2,146
Photonics for artificial intelligence and neuromorphic computing
Shastri, B. J., Tait, A. N., Ferreira de Lima, T., Pernice, W. H. P., Bhaskaran, H., Wright, C. D., & Prucnal, P. R. (2021) · Nature Photonics · doi:10.1038/s41566-020-00754-y · Cited by 1,756
Parallel photonic information processing at gigabyte per second data rates using transient states
Brunner, D., Soriano, M. C., Mirasso, C. R., & Fischer, I. (2013) · Nature Communications · doi:10.1038/ncomms2368 · Cited by 938
All-optical neural network with nonlinear activation functions
Zuo, Y., Li, B., Zhao, Y., Jiang, Y., Chen, Y.-C., Chen, P., Jo, G.-B., Liu, J., & Du, S. (2019) · Optica · doi:10.1364/optica.6.001132 · Cited by 474
Ghosh, S., Opala, A., Matuszewski, M., Paterek, T., & Liew, T. C. H. (2019) · npj Quantum Information · doi:10.1038/s41534-019-0149-8 · Cited by 171
Metadata
- Authors
- Duport, F., Schneider, B., Smerieri, A., Haelterman, M., & Massar, S.
- Venue
- Optics Express
- Publisher
- Optica Publishing Group
- Published
- 2012-09-20
- Volume
- 20
- Issue
- 20
- Pages
- 22783
- ISSN
- 1094-4087 (ISSN-L)
- OpenAlex
- W2021890002
- Cited by
- 502
- References
- 3
- FWCI
- 8.87
- Type
- journal article
- Language
- en
- Open access
- gold
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