Digital electronic neural networks with analog nanophotonic frontends: a numerical study

Ravi S. Hegde

doi:10.1117/12.2573416

10 October 2020 Digital electronic neural networks with analog nanophotonic frontends: a numerical study

Ravi S. Hegde

Author Affiliations +

Proceedings Volume 11556, Nanophotonics and Micro/Nano Optics VI; 115560I (2020) https://doi.org/10.1117/12.2573416
Event: SPIE/COS Photonics Asia, 2020, Online Only

Abstract

There is recent interest in integrating optics into neural computing systems considering the potential energy savings and speed enhancements. Optical neural networks relying on free-space diffractive effects can potentially permit the use of low resolution imagers and off load a portion of electronic computation. They are of interest in microscopy and edge computing applications. A numerical analysis of ideal few-layered diffractive optical neural networks with trainable phase masks (in real and Fourier spaces) under monochromatic coherent illumination is presented. Stacked all-dielectric transmissive metasurfaces are particularly suitable for the realization of such networks at optical frequencies. Six different kinds of networks (with and without Fourier space phase masks; electronic dense layers; and intensity-dependent optical nonlinearity) are considered for a comparative analysis. The networks are assessed on their testing accuracy on the MNIST, Fashion MNIST and the grayscale CiFAR-10 datasets. Optical variants were found to perform well on MNIST (≥96% testing accuracy) and F-MNIST (≥85%) datasets where objects are clearly demarcated from the background. However, the optical variants performed poorly on the CiFAR dataset (≈44%) in comparison to state-of-the-art electronic deep convolutional neural networks. The presence of electronic dense layers or optical nonlinearity provided marginal improvements of about 2% in test accuracy. Furthermore, network scaling by widening the phase plates and/or cascading more layers is found to only marginally improve test accuracy.

Conference Presentation

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Ravi S. Hegde "Digital electronic neural networks with analog nanophotonic frontends: a numerical study", Proc. SPIE 11556, Nanophotonics and Micro/Nano Optics VI, 115560I (10 October 2020); https://doi.org/10.1117/12.2573416

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