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Silicon photonics for neuromorphic information processing

Peter Bienstman (UGent) , Joni Dambre (UGent) , Andrew Katumba (UGent) , Matthias Freiberger (UGent) , Floris Laporte (UGent) and Alessio Lugnan (UGent)
Author
Organization
Project
Center for nano- and biophotonics (NB-Photonics)
Project
PHRESCO (PHotonic REServoir COmputing)
Abstract
We present our latest results on silicon photonics neuromorphic information processing based a.o. on techniques like reservoir computing. We will discuss aspects like scalability, novel architectures for enhanced power efficiency, as well as all-optical readout. Additionally, we will touch upon new machine learning techniques to operate these integrated readouts. Finally, we will show how these systems can be used for high-speed low-power information processing for applications like recognition of biological cells.
Keywords
RESERVOIR, SYSTEMS, photonic neuromorphic information processing, reservoir computing

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Citation

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MLA
Bienstman, Peter, Joni Dambre, Andrew Katumba, et al. “Silicon Photonics for Neuromorphic Information Processing .” Optical Data Science: Trends Shaping the Future of Photonics. Vol. 10551. Bellingham: Spie-int Soc Optical Engineering, 2018. 1–7. Print.
APA
Bienstman, Peter, Dambre, J., Katumba, A., Freiberger, M., Laporte, F., & Lugnan, A. (2018). Silicon photonics for neuromorphic information processing . OPTICAL DATA SCIENCE: TRENDS SHAPING THE FUTURE OF PHOTONICS (Vol. 10551, pp. 1–7). Presented at the Conference on Optical Data Science - Trends Shaping the Future of Photonics, Bellingham: Spie-int Soc Optical Engineering.
Chicago author-date
Bienstman, Peter, Joni Dambre, Andrew Katumba, Matthias Freiberger, Floris Laporte, and Alessio Lugnan. 2018. “Silicon Photonics for Neuromorphic Information Processing .” In Optical Data Science: Trends Shaping the Future of Photonics, 10551:1–7. Bellingham: Spie-int Soc Optical Engineering.
Chicago author-date (all authors)
Bienstman, Peter, Joni Dambre, Andrew Katumba, Matthias Freiberger, Floris Laporte, and Alessio Lugnan. 2018. “Silicon Photonics for Neuromorphic Information Processing .” In Optical Data Science: Trends Shaping the Future of Photonics, 10551:1–7. Bellingham: Spie-int Soc Optical Engineering.
Vancouver
1.
Bienstman P, Dambre J, Katumba A, Freiberger M, Laporte F, Lugnan A. Silicon photonics for neuromorphic information processing . OPTICAL DATA SCIENCE: TRENDS SHAPING THE FUTURE OF PHOTONICS. Bellingham: Spie-int Soc Optical Engineering; 2018. p. 1–7.
IEEE
[1]
P. Bienstman, J. Dambre, A. Katumba, M. Freiberger, F. Laporte, and A. Lugnan, “Silicon photonics for neuromorphic information processing ,” in OPTICAL DATA SCIENCE: TRENDS SHAPING THE FUTURE OF PHOTONICS, San Francisco, CA, 2018, vol. 10551, pp. 1–7.
@inproceedings{8578411,
  abstract     = {We present our latest results on silicon photonics neuromorphic information processing based a.o. on techniques like reservoir computing. We will discuss aspects like scalability, novel architectures for enhanced power efficiency, as well as all-optical readout. Additionally, we will touch upon new machine learning techniques to operate these integrated readouts. Finally, we will show how these systems can be used for high-speed low-power information processing for applications like recognition of biological cells.},
  articleno    = {UNSP 105510K},
  author       = {Bienstman, Peter and Dambre, Joni and Katumba, Andrew and Freiberger, Matthias and Laporte, Floris and Lugnan, Alessio},
  booktitle    = {OPTICAL DATA SCIENCE: TRENDS SHAPING THE FUTURE OF PHOTONICS},
  isbn         = {978-1-5106-1588-5},
  issn         = {0277-786X},
  keywords     = {RESERVOIR,SYSTEMS,photonic neuromorphic information processing,reservoir computing},
  language     = {eng},
  location     = {San Francisco, CA},
  pages        = {UNSP 105510K:1--UNSP 105510K:7},
  publisher    = {Spie-int Soc Optical Engineering},
  title        = {Silicon photonics for neuromorphic information processing },
  url          = {http://dx.doi.org/10.1117/12.2284391},
  volume       = {10551},
  year         = {2018},
}

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