The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Parallel photonic information processing at gigabyte per second data rates using transient states
Open accessAbstract
The increasing demands on information processing require novel computational concepts and true parallelism. Nevertheless, hardware realizations of unconventional computing approaches never exceeded a marginal existence. While the application of optics in super-computing receives reawakened interest, new concepts, partly neuro-inspired, are being considered and developed. Here we experimentally demonstrate the potential of a simple photonic architecture to process information at unprecedented data rates, implementing a learning-based approach. A semiconductor laser subject to delayed self-feedback and optical data injection is employed to solve computationally hard tasks. We demonstrate simultaneous spoken digit and speaker recognition and chaotic time-series prediction at data rates beyond 1 Gbyte/s. We identify all digits with very low classification errors and perform chaotic time-series prediction with 10% error. Our approach bridges the areas of photonic information processing, cognitive and information science. Inspired by neural networks, reservoir computing uses nonlinear transient states to perform computations, offering faster parallel information processing. Brunner et al.show a photonic approach to reservoir computing capable of simultaneous spoken digit and speaker recognition at high data rates.
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Cites
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Self-citationOptoelectronic Reservoir Computing
Paquot, Y., Duport, F., Smerieri, A., Dambre, J., Schrauwen, B., Haelterman, M., & Massar, S. (2012) · Scientific Reports · doi:10.1038/srep00287 · Cited by 783
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All-optical reservoir computing
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Cited by
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017) · Nature · doi:10.1038/nature23474 · Cited by 4,779
All-optical machine learning using diffractive deep neural networks
Lin, X., Rivenson, Y., Yardimci, N. T., Veli, M., Luo, Y., Jarrahi, M., & Ozcan, A. (2018) · Science · doi:10.1126/science.aat8084 · Cited by 2,568
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
A Survey on quantum computing technology
Gyongyosi, L., & Imre, S. (2019) · Computer Science Review · doi:10.1016/j.cosrev.2018.11.002 · Cited by 646
Metadata
- Authors
- Brunner, D., Soriano, M. C., Mirasso, C. R., & Fischer, I.
- Venue
- Nature Communications
- Published
- 2013-01-15
- Volume
- 4
- Issue
- 1
- Pages
- 1364-1364
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W1965702053
- Cited by
- 938
- References
- 20
- FWCI
- 38.28
- Type
- journal article
- Language
- en
- Open access
- hybrid
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