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Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification

Scientific reports, 2018-08, Vol.8 (1), p.12324-10, Article 12324 [Peer Reviewed Journal]

2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2018 ;ISSN: 2045-2322 ;EISSN: 2045-2322 ;DOI: 10.1038/s41598-018-30619-y ;PMID: 30120316

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  • Title:
    Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification
  • Author: Chang, Julie ; Sitzmann, Vincent ; Dun, Xiong ; Heidrich, Wolfgang ; Wetzstein, Gordon
  • Subjects: Classification ; Computer applications ; Cost control ; Neural networks ; Optics
  • Is Part Of: Scientific reports, 2018-08, Vol.8 (1), p.12324-10, Article 12324
  • Description: Convolutional neural networks (CNNs) excel in a wide variety of computer vision applications, but their high performance also comes at a high computational cost. Despite efforts to increase efficiency both algorithmically and with specialized hardware, it remains difficult to deploy CNNs in embedded systems due to tight power budgets. Here we explore a complementary strategy that incorporates a layer of optical computing prior to electronic computing, improving performance on image classification tasks while adding minimal electronic computational cost or processing time. We propose a design for an optical convolutional layer based on an optimized diffractive optical element and test our design in two simulations: a learned optical correlator and an optoelectronic two-layer CNN. We demonstrate in simulation and with an optical prototype that the classification accuracies of our optical systems rival those of the analogous electronic implementations, while providing substantial savings on computational cost.
  • Publisher: England: Nature Publishing Group
  • Language: English
  • Identifier: ISSN: 2045-2322
    EISSN: 2045-2322
    DOI: 10.1038/s41598-018-30619-y
    PMID: 30120316
  • Source: PubMed Central
    ProQuest Central
    DOAJ Directory of Open Access Journals
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