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Applied Sciences
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Neuromorphic Photonics: Current Devices, Systems and Perspectives
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Neuromorphic Photonics: Current Devices, Systems and Perspectives

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 7222

Special Issue Editors

Dr. Angelina Totovic

E-Mail Website
Guest Editor
Department of Informatics, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
Interests: neuromorphic photonics; optical communications; semiconductor optical amplifiers
Dr. Nikolaos Passalis

E-Mail Website
Guest Editor
Department of Informatics, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
Interests: deep learning; neuromorphic photonics; machine learning; representation learning

Special Issue Information

Dear Colleagues,

The urgent need to extract or create value from digital knowledge bases with huge quantities of diverse data in an automated manner, often in real-time, together with the remarkable advances in photonic integration technologies, has given birth to one of the most exciting and multidisciplinary research fields of the present day—neuromorphic photonics. With deep learning (DL)-enabled AI being all-present in the widest spectrum of fields, challenges related to throughput, latency, and energy and footprint efficiency need to be addressed by future-proof solutions. At the same time, migrating from traditionally used digital electronics to photonics challenges the existing DL architectures and training algorithms by imposing new constraints, but also opening new possibilities through nonlinearities and architectures unique for photonic domain. In recent years, a wealth of devices, systems, and algorithms have emerged: from photonic spiking neurons to perceptrons, complemented by a rich portfolio of nonlinear activations including adaptable nonlinear functions, interconnected into layers and networks of various sizes and functionalities, embraced by photonic-hardware-aware training algorithms.

The focus of this Special Issue is multifold, from photonic devices that can enable a leap in photonic neural network (PNN) performance or provide new functionalities, through novel systems for addressing a variety of NNs and their layers, up to training algorithms that are adapted to the photonic hardware fabric and address the unique challenges that arise in neuromorphic photonics. Both original research papers and review articles are welcome.

Dr. Angelina Totovic
Dr. Nikolaos Passalis
Guest Editors

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Keywords

  • neuromoprhic photonics
  • photonic neural networks
  • photonic neurons
  • photonic activation functions
  • photonic AI accelerators
  • photonic TPUs
  • PICs for vector-by-matrix multiplication
  • photonic deep learning
  • reservoir computing

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Published Papers (3 papers)

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9 pages, 830 KiB  
Article
Design of Mode-Locked Fibre Laser with Non-Linear Power and Spectrum Width Transfer Functions with a Power Threshold
by Ziyi Xie, Junsong Peng, Mariia Sorokina and Heping Zeng
Appl. Sci. 2022, 12(20), 10318; https://doi.org/10.3390/app122010318 - 13 Oct 2022
Cited by 1 | Viewed by 1836
Abstract
There is a growing demand for higher computational speed and energy efficiency of machine learning approaches and, in particular, neural networks. Optical implementation of neural networks can address this challenge. Compared to other neuromorphic platforms, fibre-based technologies can unlock a wide bandwidth window [...] Read more.
There is a growing demand for higher computational speed and energy efficiency of machine learning approaches and, in particular, neural networks. Optical implementation of neural networks can address this challenge. Compared to other neuromorphic platforms, fibre-based technologies can unlock a wide bandwidth window and offer flexibility in dimensionality and complexity. Moreover, fibre represents a well-studied, low-cost and low-loss material, widely used for signal processing and transmission. At the same time, mode-locked fibre lasers offer flexibility and control, while the mode-locking effect can be crucial for unlocking ultra-short timescales and providing ultra-fast processing. Here, we propose a mode-locked fibre laser with a non-linear power threshold in both power and spectrum. The advantage of the proposed system is a spectrum width two-branch function dependent on the input signal power. The effect is caused by a transition between two operating regimes and is governed by the input signal power. The proposed design enables receiving a non-linear transfer function in amplitude with a power threshold as an optical analogue of biological neurons with the additional advantage of a non-linear two-branch transfer function in spectrum width. The latter property is similar to the frequency-varied response dependent on stimulus properties in biological neurons. Thus, our work opens new avenues in research into novel types of artificial neurons with a frequency spectrum width variable response and, consequently, spiking neural networks and neural-rate-based coding with potential applications in optical communications and networks with flexible bandwidth, such as 5G and emerging 6G. Full article
(This article belongs to the Special Issue Neuromorphic Photonics: Current Devices, Systems and Perspectives)
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18 pages, 20447 KiB  
Article
Episodic Memory and Information Recognition Using Solitonic Neural Networks Based on Photorefractive Plasticity
by Alessandro Bile, Hamed Tari and Eugenio Fazio
Appl. Sci. 2022, 12(11), 5585; https://doi.org/10.3390/app12115585 - 31 May 2022
Cited by 7 | Viewed by 1751
Abstract
Neuromorphic models are proving capable of performing complex machine learning tasks, overcoming the structural limitations imposed by software algorithms and electronic architectures. Recently, both supervised and unsupervised learnings were obtained in photonic neurons by means of spatial-soliton-waveguide X-junctions. This paper investigates the behavior [...] Read more.
Neuromorphic models are proving capable of performing complex machine learning tasks, overcoming the structural limitations imposed by software algorithms and electronic architectures. Recently, both supervised and unsupervised learnings were obtained in photonic neurons by means of spatial-soliton-waveguide X-junctions. This paper investigates the behavior of networks based on these solitonic neurons, which are capable of performing complex tasks such as bit-to-bit information memorization and recognition. By exploiting photorefractive nonlinearity as if it were a biological neuroplasticity, the network modifies and adapts to the incoming signals, memorizing and recognizing them (photorefractive plasticity). The information processing and storage result in a plastic modification of the network interconnections. Theoretical description and numerical simulation of solitonic networks are reported and applied to the processing of 4-bit information. Full article
(This article belongs to the Special Issue Neuromorphic Photonics: Current Devices, Systems and Perspectives)
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11 pages, 430 KiB  
Article
Parallel Extreme Learning Machines Based on Frequency Multiplexing
by Alessandro Lupo and Serge Massar
Appl. Sci. 2022, 12(1), 214; https://doi.org/10.3390/app12010214 - 27 Dec 2021
Cited by 3 | Viewed by 2690
Abstract
In a recent work, we reported on an Extreme Learning Machine (ELM) implemented in a photonic system based on frequency multiplexing, where each wavelength of the light encodes a different neuron state. In the present work, we experimentally demonstrate the parallelization potentialities of [...] Read more.
In a recent work, we reported on an Extreme Learning Machine (ELM) implemented in a photonic system based on frequency multiplexing, where each wavelength of the light encodes a different neuron state. In the present work, we experimentally demonstrate the parallelization potentialities of this approach. We show that multiple frequency combs centered on different frequencies can copropagate in the same system, resulting in either multiple independent ELMs executed in parallel on the same substrate or a single ELM with an increased number of neurons. We experimentally tested the performances of both these operation modes on several classification tasks, employing up to three different light sources, each of which generates an independent frequency comb. We also numerically evaluated the performances of the system in configurations containing up to 15 different light sources. Full article
(This article belongs to the Special Issue Neuromorphic Photonics: Current Devices, Systems and Perspectives)
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