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Applied Sciences
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Photonics for Optical Computing
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Photonics for Optical Computing

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 9972

Special Issue Editors

Dr. Ripalta (Patty) Stabile

E-Mail Website
Guest Editor
Institute for Photonic Integration, Technische Universiteit Eindhoven, Eindhoven, The Netherlands
Interests: integrated photonics; large-scale switch matrices; all-optical neural networks; photonic integrated cross-connects for DNN
Dr. Christos (Chris) Vagionas

E-Mail Website
Guest Editor
Department of Informatics, Aristotle University of Thessaloniki, 54453 Thessaloniki, Greece
Interests: optical interconnects; integrated photonics; integrated photonic meshes; optical memories; 5G networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Photonic and optical computing is attracting a growing amount of interest as it promises to provide massive computational parallelism, therefore offering orders-of-magnitude improvements in speed and energy efficiency over digital electronics and enabling, for example, empowering of artificial intelligence.

This Special Issue on “Photonics for Optical Computing” aims to cover recent advances in the design, realization, and demonstration of optical computation via discrete optics, diffractive optics, photonics, integrated photonics, meshes, and matrices of repeated elements, which allow linear and/or nonlinear function implementation. This also includes photonics and integrated photonics with time-stretching techniques for wide-band data processing as well as Fourier transform implementation.

Photonics for optical computation in applications where ultrafast computing or real-time processing or low power consumption or low-latency or wide-band signal processing matter are included are welcome.

This issue includes both theoretical and experimental contributions, which include a scalability analysis and show the photonic engine embedded in the digital/analog electronics environment and interfacing with the outside world.

Dr. Ripalta (Patty) Stabile
Dr. Christos (Chris) Vagionas
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • optical signal processing
  • photonics
  • integrated photonics
  • ultrafast processing
  • low-power signal processing
  • wide-band signal processing
  • brain-inspired optical computation

Published Papers (4 papers)

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Research

11 pages, 3486 KiB  
Article
An Integrated Photorefractive Analog Matrix-Vector Multiplier for Machine Learning
by Elger A. Vlieg, Lucas Talandier, Roger Dangel, Folkert Horst and Bert J. Offrein
Appl. Sci. 2022, 12(9), 4226; https://0-doi-org.brum.beds.ac.uk/10.3390/app12094226 - 22 Apr 2022
Cited by 4 | Viewed by 1858
Abstract
AI is fueling explosive growth in compute demand that traditional digital chip architectures cannot keep up with. Analog crossbar arrays enable power efficient synaptic signal processing with linear scaling on neural network size. We present a photonic photorefractive crossbar array for neural network [...] Read more.
AI is fueling explosive growth in compute demand that traditional digital chip architectures cannot keep up with. Analog crossbar arrays enable power efficient synaptic signal processing with linear scaling on neural network size. We present a photonic photorefractive crossbar array for neural network training and inference on local analog memory. We discuss the concept and present results based on the first prototype hardware. Full article
(This article belongs to the Special Issue Photonics for Optical Computing)
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15 pages, 2526 KiB  
Article
Photonic Integrated Reconfigurable Linear Processors as Neural Network Accelerators
by Lorenzo De Marinis, Marco Cococcioni, Odile Liboiron-Ladouceur, Giampiero Contestabile, Piero Castoldi and Nicola Andriolli
Appl. Sci. 2021, 11(13), 6232; https://0-doi-org.brum.beds.ac.uk/10.3390/app11136232 - 05 Jul 2021
Cited by 20 | Viewed by 3051
Abstract
Reconfigurable linear optical processors can be used to perform linear transformations and are instrumental in effectively computing matrix–vector multiplications required in each neural network layer. In this paper, we characterize and compare two thermally tuned photonic integrated processors realized in silicon-on-insulator and silicon [...] Read more.
Reconfigurable linear optical processors can be used to perform linear transformations and are instrumental in effectively computing matrix–vector multiplications required in each neural network layer. In this paper, we characterize and compare two thermally tuned photonic integrated processors realized in silicon-on-insulator and silicon nitride platforms suited for extracting feature maps in convolutional neural networks. The reduction in bit resolution when crossing the processor is mainly due to optical losses, in the range 2.3–3.3 for the silicon-on-insulator chip and in the range 1.3–2.4 for the silicon nitride chip. However, the lower extinction ratio of Mach–Zehnder elements in the latter platform limits their expressivity (i.e., the capacity to implement any transformation) to 75%, compared to 97% of the former. Finally, the silicon-on-insulator processor outperforms the silicon nitride one in terms of footprint and energy efficiency. Full article
(This article belongs to the Special Issue Photonics for Optical Computing)
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12 pages, 638 KiB  
Article
Neuro-Inspired Computing with Spin-VCSELs
by Krishan Harkhoe, Guy Verschaffelt and Guy Van der Sande
Appl. Sci. 2021, 11(9), 4232; https://0-doi-org.brum.beds.ac.uk/10.3390/app11094232 - 07 May 2021
Cited by 14 | Viewed by 1854
Abstract
Delay-based reservoir computing (RC), a neuromorphic computing technique, has gathered lots of interest, as it promises compact and high-speed RC implementations. To further boost the computing speeds, we introduce and study an RC setup based on spin-VCSELs, thereby exploiting the high polarization modulation [...] Read more.
Delay-based reservoir computing (RC), a neuromorphic computing technique, has gathered lots of interest, as it promises compact and high-speed RC implementations. To further boost the computing speeds, we introduce and study an RC setup based on spin-VCSELs, thereby exploiting the high polarization modulation speed inherent to these lasers. Based on numerical simulations, we benchmarked this setup against state-of-the-art delay-based RC systems and its parameter space was analyzed for optimal performance. The high modulation speed enabled us to have more virtual nodes in a shorter time interval. However, we found that at these short time scales, the delay time and feedback rate heavily influence the nonlinear dynamics. Therefore, and contrary to other laser-based RC systems, the delay time has to be optimized in order to obtain good RC performances. We achieved state-of-the-art performances on a benchmark timeseries prediction task. This spin-VCSEL-based RC system shows a ten-fold improvement in processing speed, which can further be enhanced in a straightforward way by increasing the birefringence of the VCSEL chip. Full article
(This article belongs to the Special Issue Photonics for Optical Computing)
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18 pages, 3216 KiB  
Article
Time-Multiplexed Spiking Convolutional Neural Network Based on VCSELs for Unsupervised Image Classification
by Menelaos Skontranis, George Sarantoglou, Stavros Deligiannidis, Adonis Bogris and Charis Mesaritakis
Appl. Sci. 2021, 11(4), 1383; https://0-doi-org.brum.beds.ac.uk/10.3390/app11041383 - 03 Feb 2021
Cited by 6 | Viewed by 2070
Abstract
In this work, we present numerical results concerning a multilayer “deep” photonic spiking convolutional neural network, arranged so as to tackle a 2D image classification task. The spiking neurons used are typical two-section quantum-well vertical-cavity surface-emitting lasers that exhibit isomorphic behavior to biological [...] Read more.
In this work, we present numerical results concerning a multilayer “deep” photonic spiking convolutional neural network, arranged so as to tackle a 2D image classification task. The spiking neurons used are typical two-section quantum-well vertical-cavity surface-emitting lasers that exhibit isomorphic behavior to biological neurons, such as integrate-and-fire excitability and timing encoding. The isomorphism of the proposed scheme to biological networks is extended by replicating the retina ganglion cell for contrast detection in the photonic domain and by utilizing unsupervised spike dependent plasticity as the main training technique. Finally, in this work we also investigate the possibility of exploiting the fast carrier dynamics of lasers so as to time-multiplex spatial information and reduce the number of physical neurons used in the convolutional layers by orders of magnitude. This last feature unlocks new possibilities, where neuron count and processing speed can be interchanged so as to meet the constraints of different applications. Full article
(This article belongs to the Special Issue Photonics for Optical Computing)
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