Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Comprehensive Survey on Nanophotonics Neural Networks

Version 1 : Received: 23 October 2021 / Approved: 25 October 2021 / Online: 25 October 2021 (13:45:12 CEST)

A peer-reviewed article of this Preprint also exists.

Demertzis, K.; Papadopoulos, G.D.; Iliadis, L.; Magafas, L. A Comprehensive Survey on Nanophotonic Neural Networks: Architectures, Training Methods, Optimization, and Activations Functions. Sensors 2022, 22, 720, doi:10.3390/s22030720. Demertzis, K.; Papadopoulos, G.D.; Iliadis, L.; Magafas, L. A Comprehensive Survey on Nanophotonic Neural Networks: Architectures, Training Methods, Optimization, and Activations Functions. Sensors 2022, 22, 720, doi:10.3390/s22030720.

Abstract

: In the last years, materializations of neuromorphic circuits based on nanophotonic arrangements have been proposed, which contain complete optical circuits, laser, photodetectors, photonic crystals, optical fibers, flat waveguides, and other passive optical elements of nanostructured materials, which eliminate the time of simultaneous processing of big groups of data, taking advantage of the quantum perspective and thus highly increasing the potentials of contemporary intelligent computational systems. This article is an effort to record and study the research that has been conducted concerning the methods of development and materialization of neuromorphic circuits of Neural Networks of nanophotonic arrangements. In particular, an investigative study of the methods of developing nanophotonic neuromorphic processors, their originality in neuronic architectural structure, their training methods and their optimization has been realized along with the study of special issues such as optical activation functions and cost functions.

Keywords

Nanophotonics Neural Networks; Optical Neural Networks; Optical Interference Unit; Optical Nonlinear Unit; Optical Activation Function; Optical Cost Function, non von Neumann Architecture.

Subject

Computer Science and Mathematics, Computer Science

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