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Optical information processing: advances in nanophotonic reservoir computing

Martin Fiers (UGent) , Kristof Vandoorne (UGent) , Thomas Van Vaerenbergh (UGent) , Joni Dambre (UGent) , Benjamin Schrauwen (UGent) and Peter Bienstman (UGent)
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Abstract
We present a complex network of interconnected optical structures for information processing. This network is an implementation of reservoir computing, a novel method in the field of machine learning. Reservoir computing can be used for example in classification problems such as speech and image recognition, or for the generation of arbitrary patterns, tasks which are usually very hard to generalize. A nanophotonic reservoir can be constructed to perform optical signal processing. Previously, simulations demonstrated that a reservoir consisting of Semiconductor Optical Amplifiers (SOA) can outperform traditional software-based reservoirs for a speech task. Here we propose a network of coupled photonic crystal cavities. Because of the resonating behaviour, a lot of power is stored in the cavity, which gives rise to interesting nonlinear effects. Simulations are done using a novel software tool developed at Ghent University, called Caphe. We train this network of coupled resonators to generate a periodic pattern using a technique called FORCE. It is shown that photonic reservoirs can outperform classical software-based reservoirs on a pattern generation task.
Keywords
photonic crystal cavities, nonlinear dynamics, coupled resonators, pattern generation, nanophotonic reservoir computing

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Citation

Please use this url to cite or link to this publication:

Chicago
Fiers, Martin, Kristof Vandoorne, Thomas Van Vaerenbergh, Joni Dambre, Benjamin Schrauwen, and Peter Bienstman. 2012. “Optical Information Processing: Advances in Nanophotonic Reservoir Computing.” In International Conference on Transparent Optical Networks-ICTON. New York, NY, USA: IEEE.
APA
Fiers, M., Vandoorne, K., Van Vaerenbergh, T., Dambre, J., Schrauwen, B., & Bienstman, P. (2012). Optical information processing: advances in nanophotonic reservoir computing. International Conference on Transparent Optical Networks-ICTON. Presented at the 14th International conference on Transparent Optical Networks (ICTON 2012), New York, NY, USA: IEEE.
Vancouver
1.
Fiers M, Vandoorne K, Van Vaerenbergh T, Dambre J, Schrauwen B, Bienstman P. Optical information processing: advances in nanophotonic reservoir computing. International Conference on Transparent Optical Networks-ICTON. New York, NY, USA: IEEE; 2012.
MLA
Fiers, Martin, Kristof Vandoorne, Thomas Van Vaerenbergh, et al. “Optical Information Processing: Advances in Nanophotonic Reservoir Computing.” International Conference on Transparent Optical Networks-ICTON. New York, NY, USA: IEEE, 2012. Print.
@inproceedings{2996153,
  abstract     = {We present a complex network of interconnected optical structures for information processing. This network is an implementation of reservoir computing, a novel method in the field of machine learning. Reservoir computing can be used for example in classification problems such as speech and image recognition, or for the generation of arbitrary patterns, tasks which are usually very hard to generalize. A nanophotonic reservoir can be constructed to perform optical signal processing. Previously, simulations demonstrated that a reservoir consisting of Semiconductor Optical Amplifiers (SOA) can outperform traditional software-based reservoirs for a speech task.
Here we propose a network of coupled photonic crystal cavities. Because of the resonating behaviour, a lot of power is stored in the cavity, which gives rise to interesting nonlinear effects. Simulations are done using a novel software tool developed at Ghent University, called Caphe. We train this network of coupled resonators to generate a periodic pattern using a technique called FORCE. It is shown that photonic reservoirs can outperform classical software-based reservoirs on a pattern generation task.},
  author       = {Fiers, Martin and Vandoorne, Kristof and Van Vaerenbergh, Thomas and Dambre, Joni and Schrauwen, Benjamin and Bienstman, Peter},
  booktitle    = {International Conference on Transparent Optical Networks-ICTON},
  isbn         = {9781467322270},
  issn         = {2162-7339},
  keyword      = {photonic crystal cavities,nonlinear dynamics,coupled resonators,pattern generation,nanophotonic reservoir computing},
  language     = {eng},
  location     = {Coventry, UK},
  pages        = {3},
  publisher    = {IEEE},
  title        = {Optical information processing: advances in nanophotonic reservoir computing},
  url          = {http://dx.doi.org/10.1109/ICTON.2012.6253889},
  year         = {2012},
}

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