The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Experimental demonstration of reservoir computing on a silicon photonics chip
Open accessAbstract
In today’s age, companies employ machine learning to extract information from large quantities of data. One of those techniques, reservoir computing (RC), is a decade old and has achieved state-of-the-art performance for processing sequential data. Dedicated hardware realizations of RC could enable speed gains and power savings. Here we propose the first integrated passive silicon photonics reservoir. We demonstrate experimentally and through simulations that, thanks to the RC paradigm, this generic chip can be used to perform arbitrary Boolean logic operations with memory as well as 5-bit header recognition up to 12.5 Gbit s−1, without power consumption in the reservoir. It can also perform isolated spoken digit recognition. Our realization exploits optical phase for computing. It is scalable to larger networks and much higher bitrates, up to speeds >100 Gbit s−1. These results pave the way for the application of integrated photonic RC for a wide range of applications. Reservoir computing uses computational techniques related to neural networks to perform certain computing tasks. Here, the authors implement a passive optical reservoir computing scheme integrated on a silicon chip, operating at speeds up to 12.5 Gbit s−1.
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Cited by
Deep learning with coherent nanophotonic circuits
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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
All-optical spiking neurosynaptic networks with self-learning capabilities
Feldmann, J., Youngblood, N., Wright, C. D., Bhaskaran, H., & Pernice, W. H. P. (2019) · Nature · doi:10.1038/s41586-019-1157-8 · Cited by 1,529
11 TOPS photonic convolutional accelerator for optical neural networks
Xu, X., Tan, M., Corcoran, B., Wu, J., Boes, A., Nguyen, T. G., Chu, S. T., Little, B. E., Hicks, D. G., Morandotti, R., et al. (2021) · Nature · doi:10.1038/s41586-020-03063-0 · Cited by 1,518
Metadata
- Authors
- Vandoorne, K., Mechet, P., Van Vaerenbergh, T., Fiers, M., Morthier, G., Verstraeten, D., Schrauwen, B., Dambre, J., & Bienstman, P.
- Venue
- Nature Communications
- Published
- 2014-03-24
- Volume
- 5
- Issue
- 1
- Pages
- 3541-3541
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2029939668
- Cited by
- 892
- References
- 26
- FWCI
- 28.72
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Vandoorne, K., Mechet, P., Van Vaerenbergh, T., Fiers, M., Morthier, G., Verstraeten, D., Schrauwen, B., Dambre, J., & Bienstman, P. (2014). Experimental demonstration of reservoir computing on a silicon photonics chip. Nature Communications. https://doi.org/10.1038/ncomms4541
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