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All-optical spiking neurosynaptic networks with self-learning capabilities

Nature volume 569, pages 208–214 (2019)Cite this article

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Abstract

Software implementations of brain-inspired computing underlie many important computational tasks, from image processing to speech recognition, artificial intelligence and deep learning applications. Yet, unlike real neural tissue, traditional computing architectures physically separate the core computing functions of memory and processing, making fast, efficient and low-energy computing difficult to achieve. To overcome such limitations, an attractive alternative is to design hardware that mimics neurons and synapses. Such hardware, when connected in networks or neuromorphic systems, processes information in a way more analogous to brains. Here we present an all-optical version of such a neurosynaptic system, capable of supervised and unsupervised learning. We exploit wavelength division multiplexing techniques to implement a scalable circuit architecture for photonic neural networks, successfully demonstrating pattern recognition directly in the optical domain. Such photonic neurosynaptic networks promise access to the high speed and high bandwidth inherent to optical systems, thus enabling the direct processing of optical telecommunication and visual data.

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Fig. 1: All-optical spiking neuronal circuits.
Fig. 2: Spike generation and operation of the artificial neuron.
Fig. 3: Supervised and unsupervised learning with phase-change all-optical neurons.
Fig. 4: Scaling architecture for all-optical neural networks.
Fig. 5: Experimental realization of a single-layer spiking neural network.

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Data availability

All data used in this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This research was supported by EPSRC via grants EP/J018694/1, EP/M015173/1 and EP/M015130/1 in the UK and the Deutsche Forschungsgemeinschaft (DFG) grant PE 1832/5-1 in Germany. W.H.P.P. acknowledges support by the European Research Council through grant 724707. We acknowledge funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 780848 (Fun-COMP).

Reviewer information

Nature thanks Geoffrey Burr, Ingo Fischer and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Authors and Affiliations

  1. Institute of Physics, University of Münster, Münster, Germany

    J. Feldmann & W. H. P. Pernice

  2. Department of Materials, University of Oxford, Oxford, UK

    N. Youngblood & H. Bhaskaran

  3. Department of Engineering, University of Exeter, Exeter, UK

    C. D. Wright

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  1. J. Feldmann
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  5. W. H. P. Pernice
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Contributions

W.H.P.P., H.B. and C.D.W. conceived the experiment. J.F. fabricated the devices (with assistance from N.Y.). N.Y. performed the deposition of the GST material. J.F. implemented the measurement setup and carried out the measurements (with help from N.Y.). All authors discussed the data and wrote the manuscript together.

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Correspondence to W. H. P. Pernice.

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Supplementary Information

This file contains Supplementary Tables 2-4 and Supplementary Figures 1-2.

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Feldmann, J., Youngblood, N., Wright, C.D. et al. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569, 208–214 (2019). https://doi.org/10.1038/s41586-019-1157-8

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