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A Power-Efficient Architecture for On-Chip Reservoir Computing

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Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions (ICANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11731))

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Abstract

Reservoir computing is a neuromorphic computing paradigm which is well suited for hardware implementations. In this work, an enhanced reservoir architecture is introduced as to lower the losses and improve mixing behaviour in silicon photonic reservoir computing designs.

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References

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Acknowledgements

This work was supported in part by the EU project PHRESCO H2020-ICT-2015-688579 and in part by the Research Foundation Flanders (FWO) under Grant 1S32818N.

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Authors and Affiliations

  1. Photonics Research Group, Department of Information Technology, Ghent University - imec, Ghent, Belgium

    Stijn Sackesyn, Chonghuai Ma, Andrew Katumba & Peter Bienstman

  2. Center for Nano- and Biophotonics (NB-Photonics), Ghent University, Ghent, Belgium

    Stijn Sackesyn, Chonghuai Ma, Andrew Katumba & Peter Bienstman

  3. IDLab, Department of Information Technology, Ghent University - imec, Ghent, Belgium

    Joni Dambre

Authors
  1. Stijn Sackesyn
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  2. Chonghuai Ma
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  3. Andrew Katumba
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  4. Joni Dambre
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  5. Peter Bienstman
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Corresponding author

Correspondence to Stijn Sackesyn .

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Editors and Affiliations

  1. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Igor V. Tetko

  2. Institute of Computer Science, Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  3. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Pavel Karpov

  4. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Fabian Theis

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Sackesyn, S., Ma, C., Katumba, A., Dambre, J., Bienstman, P. (2019). A Power-Efficient Architecture for On-Chip Reservoir Computing. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions. ICANN 2019. Lecture Notes in Computer Science(), vol 11731. Springer, Cham. https://doi.org/10.1007/978-3-030-30493-5_16

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  • DOI: https://doi.org/10.1007/978-3-030-30493-5_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30492-8

  • Online ISBN: 978-3-030-30493-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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