Citation: 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.
https://doi.org/10.3390/s22030720
Academic Editor: Steve Ling
Received: 29 December 2021
Accepted: 17 January 2022
Published: 18 January 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
A Comprehensive Survey on Nanophotonic Neural Networks:
Architectures, Training Methods, Optimization, and
Activations Functions
Konstantinos Demertzis
1,2,
* , Georgios D. Papadopoulos
1
, Lazaros Iliadis
3
and Lykourgos Magafas
1
1
Department of Physics, Faculty of Sciences, Kavala Campus, International Hellenic University, St. Loukas,
654 04 Kavala, Greece; gebapad@teiemt.gr (G.D.P.); lmagafas@otenet.gr (L.M.)
2
School of Science & Technology, Informatics Studies, Hellenic Open University, 263 35 Patra, Greece
3
School of Civil Engineering, Faculty of Mathematics Programming and General Courses, Democritus
University of Thrace, Kimmeria, 691 00 Xanthi, Greece; liliadis@civil.duth.gr
* Correspondence: kdemertz@fmenr.duth.gr or kdemertzis@teiemt.gr
Abstract:
In the last years, materializations of neuromorphic circuits based on nanophotonic arrange-
ments have been proposed, which contain complete optical circuits, laser, photodetectors, photonic
crystals, optical fibers, flat waveguides and other passive optical elements of nanostructured mate-
rials, 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 con-
ducted 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 was realized along with the study of special
issues such as optical activation functions and cost functions. The main contribution of this research
work is that it is the first time in the literature that the most well-known architectures, training
methods, optimization and activations functions of the nanophotonic networks are presented in a
single paper. This study also includes an extensive detailed meta-review analysis of the advantages
and disadvantages of nanophotonic networks.
Keywords:
nanophotonic neural networks; photonic neural networks; optical neural networks;
optical interference unit; optical non-linear unit; optical activation function
1. Introduction
Artificial intelligence (AI) [
1
] enables machines to be trained so as to perform particular
tasks, learn from experience, adapt to or interact with the environment and perform
realistic anthropomorphic tasks [
2
]. Contemporary AI is one of the fastest evolving fields of
information technology, in which high-level algorithmic approaches and tools descending
from applied math’s and engineering are used [
3
–
5
]. Most AI applications—from computers
playing chess to automatically driven cars—are based to a great extent on the intelligent
technologies of neural networks (NNs) [
6
] for the processing of multidimensional big data,
with a view to revealing the hidden knowledge that is included in these groups [7,8].
In classic von Neumann architecture, where the computations are restrained by the
speed of the channel between computation and memory (also known as the von Neumann
congestion), even the important innovations on problems such as the shrinking of complete
circuits, the reduction in their power needs and the decrease of temperature emitted by
them cannot achieve the anticipated increases in their computing power [
9
,
10
]. Even
with the introduction of a graphics processing unit (GPU) as an extra processor for the
improvement of graphic interface and the performance of tasks of high-level processing, or
Sensors 2022, 22, 720. https://doi.org/10.3390/s22030720 https://www.mdpi.com/journal/sensors
