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Optical signal processing with a network of semiconductor optical amplifiers in the context of photonic reservoir computing

Kristof Vandoorne (UGent) , Martin Fiers (UGent) , David Verstraeten, Benjamin Schrauwen (UGent) , Joni Dambre (UGent) and Peter Bienstman (UGent)
(2011) Proceedings of SPIE, the International Society for Optical Engineering. 7972.
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Abstract
Photonic reservoir computing is a hardware implementation of the concept of reservoir computing which comes from the field of machine learning and artificial neural networks. This concept is very useful for solving all kinds of classification and recognition problems. Examples are time series prediction, speech and image recognition. Reservoir computing often competes with the state-of-the-art. Dedicated photonic hardware would offer advantages in speed and power consumption. We show that a network of coupled semiconductor optical amplifiers can be used as a reservoir by using it on a benchmark isolated words recognition task. The results are comparable to existing software implementations and fabrication tolerances can actually improve the robustness.
Keywords
semiconductor optical amplifiers, reservoir computing, speech recognition, integrated optics, RECOGNITION, optical neural networks

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Citation

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MLA
Vandoorne, Kristof, et al. “Optical Signal Processing with a Network of Semiconductor Optical Amplifiers in the Context of Photonic Reservoir Computing.” Proceedings of SPIE, the International Society for Optical Engineering, edited by Louay A Eldada and El-Hang Lee, vol. 7972, SPIE, the International Society for Optical Engineering, 2011, doi:10.1117/12.874165.
APA
Vandoorne, K., Fiers, M., Verstraeten, D., Schrauwen, B., Dambre, J., & Bienstman, P. (2011). Optical signal processing with a network of semiconductor optical amplifiers in the context of photonic reservoir computing. In L. A. Eldada & E.-H. Lee (Eds.), Proceedings of SPIE, the International Society for Optical Engineering (Vol. 7972). https://doi.org/10.1117/12.874165
Chicago author-date
Vandoorne, Kristof, Martin Fiers, David Verstraeten, Benjamin Schrauwen, Joni Dambre, and Peter Bienstman. 2011. “Optical Signal Processing with a Network of Semiconductor Optical Amplifiers in the Context of Photonic Reservoir Computing.” In Proceedings of SPIE, the International Society for Optical Engineering, edited by Louay A Eldada and El-Hang Lee. Vol. 7972. Bellingham, WA, USA: SPIE, the International Society for Optical Engineering. https://doi.org/10.1117/12.874165.
Chicago author-date (all authors)
Vandoorne, Kristof, Martin Fiers, David Verstraeten, Benjamin Schrauwen, Joni Dambre, and Peter Bienstman. 2011. “Optical Signal Processing with a Network of Semiconductor Optical Amplifiers in the Context of Photonic Reservoir Computing.” In Proceedings of SPIE, the International Society for Optical Engineering, ed by. Louay A Eldada and El-Hang Lee. Vol. 7972. Bellingham, WA, USA: SPIE, the International Society for Optical Engineering. doi:10.1117/12.874165.
Vancouver
1.
Vandoorne K, Fiers M, Verstraeten D, Schrauwen B, Dambre J, Bienstman P. Optical signal processing with a network of semiconductor optical amplifiers in the context of photonic reservoir computing. In: Eldada LA, Lee E-H, editors. Proceedings of SPIE, the International Society for Optical Engineering. Bellingham, WA, USA: SPIE, the International Society for Optical Engineering; 2011.
IEEE
[1]
K. Vandoorne, M. Fiers, D. Verstraeten, B. Schrauwen, J. Dambre, and P. Bienstman, “Optical signal processing with a network of semiconductor optical amplifiers in the context of photonic reservoir computing,” in Proceedings of SPIE, the International Society for Optical Engineering, San Francisco, CA, USA, 2011, vol. 7972.
@inproceedings{1958435,
  abstract     = {{Photonic reservoir computing is a hardware implementation of the concept of reservoir computing which comes from the field of machine learning and artificial neural networks. This concept is very useful for solving all kinds of classification and recognition problems. Examples are time series prediction, speech and image recognition. Reservoir computing often competes with the state-of-the-art. Dedicated photonic hardware would offer advantages in speed and power consumption. We show that a network of coupled semiconductor optical amplifiers can be used as a reservoir by using it on a benchmark isolated words recognition task. The results are comparable to existing software implementations and fabrication tolerances can actually improve the robustness.}},
  articleno    = {{79420P}},
  author       = {{Vandoorne, Kristof and Fiers, Martin and Verstraeten, David and Schrauwen, Benjamin and Dambre, Joni and Bienstman, Peter}},
  booktitle    = {{Proceedings of SPIE, the International Society for Optical Engineering}},
  editor       = {{Eldada, Louay A and Lee, El-Hang}},
  isbn         = {{9780819484796}},
  issn         = {{0277-786X}},
  keywords     = {{semiconductor optical amplifiers,reservoir computing,speech recognition,integrated optics,RECOGNITION,optical neural networks}},
  language     = {{eng}},
  location     = {{San Francisco, CA, USA}},
  pages        = {{7}},
  publisher    = {{SPIE, the International Society for Optical Engineering}},
  title        = {{Optical signal processing with a network of semiconductor optical amplifiers in the context of photonic reservoir computing}},
  url          = {{http://doi.org/10.1117/12.874165}},
  volume       = {{7972}},
  year         = {{2011}},
}
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