• Photonics Research
  • Vol. 10, Issue 12, 2846 (2022)
Carlo M. Valensise1, Ivana Grecco2, Davide Pierangeli1、2、3、*, and Claudio Conti1、2、3
Author Affiliations
  • 1Enrico Fermi Research Center (CREF), 00184 Rome, Italy
  • 2Physics Department, Sapienza University of Rome, 00185 Rome, Italy
  • 3Institute for Complex Systems, National Research Council (ISC-CNR), 00185 Rome, Italy
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    DOI: 10.1364/PRJ.472932 Cite this Article Set citation alerts
    Carlo M. Valensise, Ivana Grecco, Davide Pierangeli, Claudio Conti. Large-scale photonic natural language processing[J]. Photonics Research, 2022, 10(12): 2846 Copy Citation Text show less

    Abstract

    Modern machine-learning applications require huge artificial networks demanding computational power and memory. Light-based platforms promise ultrafast and energy-efficient hardware, which may help realize next-generation data processing devices. However, current photonic networks are limited by the number of input-output nodes that can be processed in a single shot. This restricted network capacity prevents their application to relevant large-scale problems such as natural language processing. Here, we realize a photonic processor for supervised learning with a capacity exceeding 1.5×1010 optical nodes, more than one order of magnitude larger than any previous implementation, which enables photonic large-scale text encoding and classification. By exploiting the full three-dimensional structure of the optical field propagating in free space, we overcome the interpolation threshold and reach the over-parameterized region of machine learning, a condition that allows high-performance sentiment analysis with a minimal fraction of training points. Our results provide a novel solution to scale up light-driven computing and open the route to photonic natural language processing.
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    Carlo M. Valensise, Ivana Grecco, Davide Pierangeli, Claudio Conti. Large-scale photonic natural language processing[J]. Photonics Research, 2022, 10(12): 2846
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