RESEARCH UNIT ON NEUROMORPHIC COMPUTING AND PHOTONICS (RNCP)

RNCP unit is a 2020 initiative for collaboration between the Computer and Communication Systems Laboratory (CCSL) of the University of the Aegean, department of Information and Communication Systems Engineering and the Parallel and Distributed Systems and Networks Lab (PDSN) of the University of West Attica, Department of Informatics and Computer Engineering. The two labs decided to unite forces and expertise forming a joint research unit specializing on photonic neuromorphic computing systems and their applications, dedicated machine-learning hardware and software techniques, optical communications and physical layer security. The group has thorough expertise gained through the participation of its members in diverse EU and national research projects in the broad area of photonics and optical communications.

REASEARCH ACTIVITIES

Photonic neuromorphic computing is a rapidly evolving scientific field which attracts the interest worldwide due to its intrinsic scientific value and the niche applications it can enable. Bio-inspired computing can be performed in photonics with the use of pulsed lasers and complex waveguide structures achieving neuron-like behavior in unprecedented speeds and low consumption. The intrinsic parallelism of photonic circuits paves the wave for enhanced connectivity and scalability so as to solve complex problems at the speed of photons. RNCP members have a strong activity in the area as can be found in the publication record and in the participation in EU and national R&D projects.

Optical communication systems suffer from linear and nonlinear effects of the optical channel. Although modern ASICs can handle linear effects such as polarization mode dispersion and chromatic dispersion, the major limitation regarding the maximum capacity that can be achieved comes from the nonlinearities attributed to Kerr effect. RCNP investigates state of the art recursive neural networks and reservoir computing techniques so as to mitigate the nonlinear effects in long-haul WDM transmission systems. The same activity seeks for applying low complexity machine learning algorithms in short-area networks where consumption matters.

One of the key areas that MCP has invested is the envision-design and development of cryptographic devices based on electronic-photonics hardware for cyber-physical applications. In particular MCP has developed multiple designs of optical modules as non-replicable authentication tokens and secure pseudo-random generators able to be integrated in IoT ecosystems and solidifying their resilience against cyber-physical attacks. More importantly MCP is working towards combining its solid background on neuromoprhic engineering and crypto-systems, so as to spawn a new generation of electronic-photonic neuro-cryptographic devices able to offer a twofold advantage. On one hand, provide solid security features, such as data encryption and authentication, based on physical properties and secondly employ the same modules for anomaly detection and edge machine learning

The use of fiber infrastructures for environmental sensing is attracting global interest due to the fact that optical fibers emerge as low cost and easily accessible platforms exhibiting a large terrestrial deployment. Moreover, optical fiber networks offer the unique advantage of providing observations of submarine areas, where the sparse existence of permanent seismic instrumentation due to cost and difficulties in deployment limits the availability of high-resolution subsea information on natural hazards in both time and space. The use of optical techniques that leverage pre-existing fiber infrastructure can efficiently provide higher resolution coverage and pave the way for the identification of the detailed structure of the Earth especially on seismogenic submarine faults. RNCP and co-workers from other institutions has developed a new fibre-optics sensing technique relying microwave frequency fibre interferometry (MFFI) which is a simple and low-cost proposition compared to the state-of-the-art solutions exploiting distributed acoustic sensing. The technique has been published in Nature Scientific Reports (https://www.nature.com/articles/s41598-022-18130-x) and attracts the interest of many geophysicists and seismologists around the globe.

RNCP PROJECTS

PROMETHEUS

PROMETHEUS’ vision: is to shatter the boundaries between quantum and neuromorphic photonic processing and merge them into a uniform integrated platform. In particular, the PROMETHEUS’ photonic integrated chip (PIC) will be based on a highly dense silicon on insulator (SOI) Field Programmable Photonic Gate Array (FPPGA) synaptic layer, strengthened by nearly-zero power-consuming non-volatile barium titanate (BTO) phase shifters and co-integrated III-V lasers, that will form an ultra-fast spiking neural layer. Sophisticated packaging, will provide a disruptive, yet robust device able to unravel large-scale neuromorphic-quantum implementations, offering unprecedentedly low power consumption, processing speed and versatility to adapt to a wide pallet of applications.


PROMETHEUS’ multi-purpose photonic platform will be suitable for the exploration of emerging concepts such as spiking networks, reservoir computing, convolutional optical networks and quantum neural networks; aiming to address two key industry-driven applications: high-speed image processing for biomedical/industrial applications (cytometry/laser scanning) and optical signal processing at the network’s edge (signal equalization). Moving a step further, PROMETHEUS’ PIC will be also utilized as a physical root of trust, unlocking its use as a quantum random number generator (QRNG) module and as a cyber-secure photonic physical unclonable function (PUF). These two operations will provide a pivotal advantage to PROMETHEUS’ modules: to act as neuromorphic processors and at the same time, to provide hardware-based authentication and security features derived from quantum key generation.

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NEoteRIC

NEuromorphic Reconfigurable Integrated
photonic Circuits as artificial image processor

An unconventional approach to demanding image applications

NEoteRIC’s primary objective is the generation of holistic photonic machine learning paradigms that will address demanding imaging applications in an unconventional approach providing paramount frame rate increase, classification performance enhancement and orders of magnitude lower power consumption compared to the state-of-the-art machine learning approaches.

NEoteRIC’s implementation stratagem incorporates multiple innovations spanning from the photonic “transistor” level and extending up to the system architectural level, thus paving new, unconventional routes to neuromorphic performance enhancement.

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NEBULA

NEuromorphic Processor Based on qUantum-Dot LAsers

Nebula Project is a research program funded by GSRT-ELIDEK (GR), hosted by the Dept. Informatics & Telecommunications of the National & Kapodistrian University of Athens and particularly the Optical Communications & Photonics Technology Laboratory, whereas it is supported by the Dept. of Photonic & Quantum Sciences of Heriot-Watt University. ​

It focus on the design, optimisation and development of fully isomorphic to biological structures photonic neurons with ultra-fast response  and marginal power consumption. These structures will be incorporated to a electro-optic scheme so as to mimic in the optical domain artificial sensorimotor processing and exploit disruptive computational paradigms like the Reservoir Computing Concept. ​

Its long-term vision is to provide an alternative path for computation harvesting the merits of photonics and neuromoprhic engineering and transforming them to a new paradigm

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SafeIT

“SafeIT – Wearable systems for the safety and wellbeing applied in security guards”, Research-Create-Innovate 2nd Cycle, National Strategic Reference Framework (NSRF) 2014-2020, 2020

Wearable devices and augmented reality are rapidly evolving and are particularly applicable in the service sector, such as safety, health, education, etc. In the field of security services, in particular the no-contact / lone security positions, device handler technology can offer innovative services. In particular, it can further secure the individual protection of staff in existing security services and improve their mental concentration and perceptual capacity, which assists in decision-making. Portable devices can detect the presence, biometric data of the guardian, and many additional elements that are useful both in the service they perform and the personal safety of the workers.

The goal of the project is to develop systems and applications that will receive and process data from smart wearable to record biometric data for the purposes of health and vital safety monitoring, positioning and presence / commencement / ending of the shift, direct notification and other required services, such as indications of danger, attack, etc. These devices will automatically wirelessly connect with additional equipment (ambient sensors and positional beacons) and their data will be combined with the cameras data. Systems of Augmented / Mixed reality will also be developed in addition to simply record and track signals, more effectively manage the data from incident management center operators and supporting decision-making by the end user (guardian).

Due to the variety of data to be transmitted and stored, a combination of modern signal processing and machine learning techniques is needed through the creation of a knowledge base. The latter will be shaped by the data collected per event. There will also be use of pioneering security techniques which combine multi-parametric authentication, such as the use of biometric elements (something the user is) with equipment components (somethingthe user has), which will be physically resistant to cloning, that is to say they will exhibit structural features that will make them unique. Some of the most important issues that will be addressed in the project are the data transmission security and the use of blockchain technology for secure data storage (protection against unauthorized data alteration, PUF assisted encryption), as well as the use of security technologies to protect personal data (use of nicknames and aliases, PUF reciprocal authentication). In the strand of augmented reality, the technological innovation that the project introduces is multidimensional. More specifically, this project is the first national effort to develop an integrated augmented/mixed reality platform that acts on both the field and the control center. The development of AR interfaces for managing information in the field of security services is an innovation of the project. Also, the development of ergonomic interfaces based on the geometric representation of the area of interest combined with the use of maps in an augmented reality environment is another innovation the project aspires to introduce. Finally, all applications that will be deployed will respect the privacy of users by complying to the requirements of innovative and academically recognized methodologies, while ensuring all the technical and organizational requirements set by the new General Data Protection Regulation 2016/679 (GCC-GDPR) are met.

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QPIC-1550

Quantum photonic integrated circuits (QPICs) operating at 1550 nm are becoming increasingly important for the development of quantum technologies due to their ability to provide compact, high-performance, and scalable systems for creation, manipulation and detection of single photons.
Single photons are the fundamental building blocks of quantum communication, computing and quantum metrology. In order to realise these quantum technologies, it is crucial to have the ability to manipulate single photons with high precision and reliability. Heterogeneous QPICs offer a promising solution to this challenge by integrating different photonic components such as waveguides, modulators, detectors, and nonlinear materials on a single chip, enabling the creation of complex photonic circuits that can perform a wide range of functions with high efficiency and low loss.
Furthermore, QPICs at 1550 nm have the advantage of operating in the telecom band, which is the standard wavelength for optical communication. This compatibility with existing optical fibre networks allows for seamless integration with existing communication infrastructure, making these QPICs a cost-effective and practical solution for the development of practical quantum technologies.
While PICs have been extensively developed and used in classical optical communication systems, the current technology is not yet fully ready for quantum technologies due to several challenges that need to be addressed.
One of the primary challenges is the need for high-quality single photon and entangled photon pair sources and detectors that can be integrated with PICs. Current technologies suffer from low efficiency, high noise levels, and they are either low-efficiency and easily integrable or better efficiency but hard to integrate on PICs. In addition, the integration of multiple components on a single chip can introduce unwanted noise and losses, which can degrade the quality of the photonic signals and reduce the overall performance of the system.
Furthermore, current PICs lack the necessary level of stability and control required for quantum technologies. For example, temperature fluctuations and vibrations can significantly affect the performance of the PICs, which can limit their usefulness in practical quantum applications.
In this project we aim to overcome those limitations by providing a universal PIC platform for quantum technologies. During the course of the project, we will develop a source of on-demand highly indistinguishable photons based on InAs/InP Quantum Dots (QD), a source of entangled photons using the same QD technology, and single photon detectors in InGaAs/InP. Those components will be integrated on a SiN platform for the manipulation and control of the photons. All working at the telecom wavelength of 1550 nm. After the success of this project, we will be able to use the platforms developed for the realisation of systems for real-world applications in the field of quantum communication, computing and time dissemination, with the advantage in cost, dimension and stability associated with PIC technology compared to the solutions currently used in those fields. RNCP will undertake the installation of a quantum clock-synchronization testbed in Athens, the evaluation of QPIC devices as physical unclonable functions in the quantum world and will assist the experiments that will demonstrate distributed quantum computing.

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QUASAR

At a glance QUASAR’s vision is to explore disruptive paradigms such as neuromorphic computing and physical unclonable functions (PUF) and merge them into an implementation agnostic, cyber physical security ecosystem. QUASAR’s technical proposition is to exploit fabrication induced “imperfections” of diverse neuromorphic schemes (digital/analogue electronics and silicon photonics), as a machine-learning (ML) related physical fingerprint. At the same time QUASAR modules will retain their neuromorphic operation to unlock simultaneous machine learning/hardware acceleration capabilities for diverse tasks.
QUASAR’s novel neuromorphic physical unclonable functions (N-PUFs) will act as a secure physical “root of trust” operating as hardware cryptographic key generators, through a non-reversible deterministic process. This feature will unleash unconventional cyber-secure ways to “store” keys in decentralized Internet of Things (IoT) networks, by “physically hiding” the keys within the neural structure instead of using conventional- unsecure digital storage components. Finally, we aim to further push the limits of technology by merging photonic N-PUFs with Quantum Photonic platforms and Quantum Random Generators to provide two ad- ditional pivotal features: quantum driven anti-tampering capabilities and true random number generation for genuine N-PUF initialization.

NOOK

Next generation Optical communication systems in the O-band: Α Key enabler for capacity enhancement in existing fibre links – NOOK

HFRI  Research Projects to Support Faculty Members & Researchers):  Next generation Optical communication systems in the O-band: Α Key enabler for capacity enhancement in existing fibre links,

NOOK, 1/1/2022-31/12/2024

– Principal Investigator (Name/Surname): Adonis Bogris
– Scientific Area: Engineering Sciences & Technology
– Scientific Field: Electrical, electronic & communication engineering
– Scientific Subfield: Communication engineering and systems
– Projects’ Duration (in months):36
– Host Institution: University of West Attica
– Collaborating Organization(s): University of Southampton (UoS), University of the Aeagean (UoA)

Optical fibre is the most broadband transmission medium comprising the highway that massively transfers data corresponding to communication taking place among billions of terminal devices per second. State of
the art optical communication systems operate in the C-band (1530 -1565 nm) and less often in the L-band (1565 -1625 nm). These two bands offer almost 100 nm of useful optical bandwidth and have been predom-inantly selected since early 80s as they provide propagation at the lowest loss. This fact was a strong driver for the fabrication of devices such as emitters, receivers and amplifiers that operate in this wavelength window. Nowadays, many experts in the field of optical communications and networking predict that the continuous need for more demanding paradigms such as 5G, Internet of Things, cloud services, etc. will fully exhaust the capacity offered by C-band and L-band, thus posing an indisputable need for enhancement of
long-haul communication systems capacity. Different techniques have been proposed to circumvent this threat, either pushing towards the ideal utilisation of fibre capacity in the non-linear regime or by introducing
spatial division multiplexing in the form of multi-fibre, multi-core and multi-mode transmission. 
Lately, the extension of fibre bandwidth as a method to resolve “capacity crunch” has started gaining ground.
For this reason, many groups worldwide focus their research on designing and fabricating optical devices that may cover other useful wavelengths for fibre transmission ranging from 1250 nm to 1700 nm. The potential of already installed fibres is to accommodate 300 nm of wavelength multiplexed signals, whilst nowadays the majority of the systems only utilise the C-band (~ 35 nm). The most important argument of the bandwidth extension roadmap is that the next generation optical communication systems will rely on already deployed infrastructures, thus avoiding the requirement for a costly infrastructure upgrade proposed by the experts
exploring the potential of few-mode fibres (FMFs) and multi-core fibres (MCFs).

ΝOOK will focus its research on exploring O-band (1260-1360 nm) which offers extra 100 nm of optical bandwidth and has a clear potential for the generation of high quality devices, including optical amplifiers with the use of properly designed Bi-doped fibres [1]. The main particular property of O-band transmission is the almost zero dispersion and its significant variation along the 100 nm which lays the ground for the manifestation of strong and non-uniform nonlinear effects that must be efficiently mitigated. Besides the in-depth analysis of conventional communications, NOOK will also take into account the advent of quantum communications and will investigate the potential of O-band in simultaneously supporting classical and quantum channels and the impact of O-band transmission on the performance of classical and quantum channels residing at C-, L-bands.

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RNCP PEOPLE

RNCP Unit Directors

<b>Adonis Bogris</b>
Adonis BogrisProfessor
Adonis Bogris was born in Athens. He received the B.S. degree in informatics, the M.Sc. degree in telecommunications, and the Ph.D. degree from the National and Kapodistrian University of Athens, Athens, in 1997, 1999, and 2005, respectively. His doctoral
thesis was on all-optical
processing by means of fiber-based devices. He is currently a Professor at the Department of
Informatics and Computer Engineering at the University of
West Attica, Greece. He has authored or co-authored more than 200 articles published in international scientific journals and conference proceedings and he has participated in plethora of
EU and national research projects. His current research interests
include high-speed all-optical transmission systems and
networks, nonlinear effects in optical fibers, all-optical signal
processing, neuromorphic photonics, mid-infrared photonics
and cryptography at the physical layer. Dr. Bogris serves as a
reviewer for the journals of the IEEE and Optica. He is an associate editor of Optics Continuum, Optica and IEEE Journal of Lightwave Technology
<b>Charis Mesaritakis</b>
Charis MesaritakisAssociate Professor
Prof. Charis Mesaritakis acquired his diplom, M.Sc and Ph.D from National and Kapodistrian Univeristy of Athens (Greece). His Ph.D thesis focused on the experimental characterization and numerical modelling of novel regimes of quantum dot mode locked lasers. He has participated as a researcher in 10 FP6-FP7 and Horizon2020 EU projects. He has been awarded a postdoctoral EU Marie-Curie Fellowship, involving high precision laser telemetry in III-V Labs (France); Followed by two competitive national research grants, PROMITHEAS from the G. Latsis foundation and HFRI-GSRT NEBULA, both focusing on the investigation of photonic neuromorphic technologies and photonic machine learning. Currently he serves as technical manager for the H2020 NEoteRIC project also focusing on photonic neuromorphic paradigms. Since 2019, he is an associate professor at the department of Information and Communication Systems Engineering at the University of the Aegean, splitting his research interest among design/implement photonic neuromorphic systems for high bandwidth applications and photonic physical layer cryptographic modules (Η2020 KONFIDO, GSRT- SAFE-IT). He is author and co-author of more than 70 publications in highly cited journals and international conferences focusing on quantum-dot laser dynamics, neuromorphic schemes and physical layer security. He is a patent holder for photonic-physical unclonable functions modules for implementing physical layer security. He serves as regular reviewer for IEEE, OSA, AIP and Springer Journals whereas he serves as guest editor for MDPI’s Applied Sciences journal.


Mesaritakis CV

RNCP Researchers – PhD Students

<b>Stavros Deligiannidis, PhD</b>
Stavros Deligiannidis, PhD
Stavros Deligiannidis holds a BSc in Physics, a MSc degree in Microelectronics and VLSI from the National and Kapodistrian University of Athens and a PhD from the University of West Attica in the field of novel signal processing techniques for optical communication systems. Since 2010, he has been at the Computer Engineering Department of the Technological Educational Institute of Peloponnese, where he served as a Lecturer. He has worked as a researcher in local and European projects. His current research interests include optical communications, deep learning, digital signal processing, and parallel computing.
<b>Dimitris Dermanis</b>
Dimitris Dermanis
Dimitris
Dermanis was born in Athens, Greece. He holds a 5 years diploma (integrated MSc) in computer science and telecommunications from the department of Information and Communication Systems Engineering (ICSD) of the University of the Aegean. He is currently pursuing
his Ph.D at the University of the Aegean, in the field of Neuromorphic Computing under the supervision of Prof. Charis Mesaritakis. His main research interests include neural networks, deep learning and cryptography.
<b>George Sarantoglou</b>
George Sarantoglou
George Sarantoglou received the Diploma degree in electrical and computer
engineering from the University of Patras, Patras, Greece, on 2016. He is
currently working towards his Ph.D. degree with the Department of Information and Communication Systems Engineering, University of the Aegean, Samos Greece. His Ph.D. thesis focuses on the experimental analysis and development of photonic processors for unconventional, bio-inspired information processing, targeting machine learning applications . His research interests include photonic systems for analog pattern recognition and multi-sensory applications.
<b>Menelaos Skontranis</b>
Menelaos Skontranis
Menelaos Skontranis received his bachelor in 2016 from Hellenic Air Force Academy as an Telecommunication-Electronic Engineer. He received his master in Microelectronics in 2019 from the Department of Informatics and Telecommunications of the National and Kapodistrian Univeristy of Athens. He currently is a PhD candidate in the Department of Information and Communication Systems Engineering of the University of Aegean under the supervision of Associate Professor Charis Mesaritakis. His research interest focuses on Quantum Dot Lasers, Vertical Cavity Surface Emitting Lasers and Optical Neural Networks.
<b>Kostas Sozos</b>
Kostas Sozos
Kostas Sozos received his B.S degree in Physics from the University of Patras in 2018 and the M.Sc in Microsystems & Nanodevices from the National Technical University of Athens in 2020. He is currently pursuing his Ph.D at the University of West Attica in the field of Neuromorphic Photonic Computing under the supervision of Prof. Adonis Bogris. His main research interests include photonic neural networks, deep learning, pattern recognition and optical communications.
<b>Giannis Tsilikas</b>
Giannis Tsilikas
Ioannis (Giannis) Tsilikas holds a 5 years Diploma (integrated MSc) in applied Physics, from School of Applied Mathematical and Physical Sciences, National Technical University of Athens. He is currently pursuing his PhD degree at the National Technical University of Athens in the field of ultra short laser pulses interaction with matter under the supervision of Prof. Charis Measaritakis and Prof. Georgios Tsigaridas. He has worked as a researcher in 5 National research projects and in 3 European research projects (up to May 2020). His current research interests include optical experimental set ups of Femto Laser Systems, photonics signal processing, short and ultra short duration laser pulses interaction with biological matter, biophotonics and photonics.
<b>Aris Tsirigotis</b>
Aris Tsirigotis
Aris Tsirigotis received his B.S. degree in Physics in 2016 and the Μ.Sc. in Electronics and Radioelectrology in 2020 from the National and Kapodistrian University of Athens.He is currently pursuing his Ph.D at the University of the Aegean in the field of Neuromorphic Photonic Computing under the supervision of Prof.Charis Mesaritakis. His main research interests include photonic neural networks, photonic design, deep learning, pattern recognition, optical communications and optical design.
<b>Giorgos Moustakas</b>
Giorgos Moustakas
Giorgos Moustakas received his Computer Science and Engineering degree (Integrated Master) from the University of West Attica in 2023. He is currently pursing his PhD degree in the field of Neuromorphic Computing under the supervision of Prof. Adonis Bogris. His current research interests include optical communications, spiking neural networks, deep learning and digital signal processing.

RNCP PUBLICATIONS

Publications in international scientific journals

  1. Sarantoglou, G., Bogris, A., & Mesaritakis, C. (2024). All-Optical, Reconfigurable and Power Independent Neural Activation Function by Means of Phase Modulation. arXiv preprint arXiv:2402.03778.
  2. Dermanis, D., Rizomiliotis, P., Bogris, A., & Mesaritakis, C. (2024). Pseudo-Random Generator based on a Photonic Neuromorphic Physical Unclonable Function. arXiv preprint arXiv:2402.14876.
  3. Sozos, K., Da Ros, F., Yankov, M. P., Sarantoglou, G., Deligiannidis, S., Mesaritakis, C., & Bogris, A. (2024). Experimental Investigation of a Recurrent Optical Spectrum Slicing Receiver for Intensity Modulation/Direct Detection systems using Programmable Photonics. arXiv preprint arXiv:2406.16869.
  4. Tsilikas, I., Tsirigotis, A., Sarantoglou, G., Deligiannidis, S., Bogris, A., Posch, C., … & Mesaritakis, C. (2024). Photonic Neuromorphic Accelerators for Event-Based Imaging Flow Cytometry. arXiv preprint arXiv:2404.10564.
  5. Tsirigotis, A., Sarantoglou, G., Deligiannidis, S., Sanchez, E., Gutierrez, A., Bogris, A., … & Mesaritakis, C. (2024). Photonic Neuromorphic Accelerator for Convolutional Neural Networks based on an Integrated Reconfigurable Mesh. arXiv preprint arXiv:2405.06434.
  6. Sozos, K., Deligiannidis, S., Mesaritakis, C., & Bogris, A. (2024). Unconventional Computing based on Four Wave Mixing in Highly Nonlinear Waveguides. IEEE Journal of Quantum Electronics.
  7. S. Deligiannidis, K. R. H. Bottrill, K. Sozos, C. Mesaritakis, P. Petropoulos and A. Bogris, “Multichannel Nonlinear Equalization in Coherent WDM Systems based on Bi-directional Recurrent Neural Networks,” in Journal of Lightwave Technology, doi: 10.1109/JLT.2023.3318559.
  8. Tsirigotis, G. Sarantoglou, S. Deligiannidis, K. Sozos, C. Mesaritakis, “Integrated Photonic Accelerator Based on Optical Spectrum Slicing for Convolutional Neural Networks”, arxiv.org:2303.10357 (2023)
  9. Sozos, S. Deligiannidis, C. Mesaritakis, A. Bogris. “Self-Coherent Receiver Based on a Recurrent Optical Spectrum Slicing Neuromorphic Accelerator” IEEE Journal of Lightwave Technology, 41, 2666-2674 (2023)
  10. Skontranis, M., Sarantoglou, G., Sozos, K., Kamalakis, T., Mesaritakis, C., & Bogris, A. (2023). Multimode fabry-perot laser as a reservoir computing and extreme learning machine photonic accelerator. Neuromorphic Computing and Engineering, 3(4), 044003.
  11. K. Sozos, S. Deligiannidis, G. Sarantoglou, C. Mesaritakis and A. Bogris, “Recurrent Neural Networks and Recurrent Optical Spectrum Slicers as Equalizers in High Symbol Rate Optical Transmission Systems,” in Journal of Lightwave Technology, vol. 41, no. 15, pp. 5037-5050, 1 Aug.1, 2023, doi: 10.1109/JLT.2023.3282999.
  12. Tsirigotis, G. Sarantoglou, M. Skontranis, S. Deligiannidis, K. Sozos, G. Tsilikas, D. Dermanis, A. Bogris, C. Mesaritakis. “Unconventional Integrated Photonic Accelerators for High-Speed Convolutional Neural Networks”. (invited) Science, Intelligence Computing, 2023:0032. DOI:10.34133/icomputing.0032
  13. Lentas, K., Bowden, D., Melis, N. S., Fichtner, A., Koroni, M., Smolinski, K., … & Simos, I. (2023). Earthquake location based on Distributed Acoustic Sensing (DAS) as a seismic array. Physics of the Earth and Planetary Interiors, 107109
  14. Sozos, A. Bogris, G. Sarantoglou, P. Bienstman, C. Mesaritakis, “High-Speed Photonic Neuromorphic Computing Using Recurrent Optical Spectrum Slicing Neural Networks” 1:(24) doi.org/10.1038/s44172-022-00024-5 Nature Communication Engineering, (2022)
  15. Bogris, T. Nikas, C. Simos, I. Simos, K. Lentas, Ν. S. Melis, A. Fichtner, D. Bowden, K. Smolinski, C. Mesaritakis & I. Chochliouros. “Sensitive seismic sensors based on microwave frequency fiber interferometry in commercially deployed cables”. Nature Scientific Reports 12, 14000 (2022)
  16. Bowden, D. C., Fichtner, A., Nikas, T., Bogris, A., Simos, C., Smolinski, K., … & Melis, N. S. (2022). Linking Distributed and Integrated Fiber‐Optic Sensing. Geophysical Research Letters, 49(16), e2022GL098727
  17. Fichtner, A., Bogris, A., Nikas, T., Bowden, D., Lentas, K., Melis, N. S., … & Smolinski, K. (2022). Theory of phase transmission fibre-optic deformation sensing. Geophysical Journal International, 231(2), 1031-1039
  18. Fichtner, A., Bogris, A., Bowden, D., Lentas, K., Melis, N. S., Nikas, T., … & Smolinski, K. (2022). Sensitivity kernels for transmission fibre optics. Geophysical Journal International, 231(2), 1040-1044.
  19. Sarantoglou, A. Bogris, C. Mesaritakis, S. Thodoridis, “Bayesian Photonic Accelarators for Energy Efficient and Noise Robust Neural Processing” (invited) IEEE Selected Topics in Quantum Electronics, 10.1109/JSTQE.2022.3183444 (2022)
  20. Dermanis, A. Bogris, P. Rizomiliotis, C. Mesaritakis, “Photonic Physical Unclonable Function based on an Integrated Neuromorphic schemes” IEEE Journal of Lightwave Technology 10.1109/JLT.2022.3200307 (2022)
  21. Skontranis, G. Sarantoglou, A. Bogris, C. Mesaritakis, “Time-Delayed Reservoir Computing Based on Dual-Waveband Quantum-Dot Spin-Polarized Vertical Cavity Surface-Emitting Laser” Optica Material Optics Express, 12(10), 4047-4060 (2022)
  22. Υ. Hong, S. Deligiannidis, N. Taengnoi, K. R. H. Bottrill, N. K. Thipparapu, Y. Wang, J. K. Sahu, David J. Richardson, C. Mesaritakis, A. Bogris, and P. Petropoulos, “ML-assisted Equalization for 50-Gb/s/λ O-band CWDM Transmission over 100-km SMF” IEEE Selected Topics in Quantum Electronics1109/JSTQE.2022.3155990 (2022).
  23. Sarantoglou, M. Skontranis, A. Bogris, C. Mesaritakis, “Experimental study of Neuromorphic Node based on a Multi- Waveband Emitting two – section Quantum Dot Laser” OSA Photonic Research, Vol. 9, No. 4 pp. B87 doi: 10.1364/PRJ.413371 (2021)
  24. Skontranis, G. Sarantoglou, S. Deligiannidis, A. Bogris, C. Mesaritakis, “Unsupervised Image Classification Through Time-Multiplexed Photonic Multi-Layer Spiking Convolutional Neural Network” Appl. Sci. 2021, 11(4) (2021)
  25. Sozos, C. Mesaritakis, A. Bogris “Monolithic Integrated Optoelectronic Reservoir Computing Processor based on Mutually Injected Phase Modulated Semiconductor Lasers” IEEE Selected Topics in Quantum Electronics accepted for publication (2021)
  26. S. Deligiannidis, C. Mesaritakis, A. Bogris, “Performance and Complexity Analysis of i-directional Recurrent Neural Network Models vs. Volterra Nonlinear Equalizers in Digital Coherent Systems”, ΙΕΕΕ Journal of Lightwave Technology , Vol.39, No.18 pp. 5791 (2021)
  27. M. Skontranis, G. Sarantoglou, S. Deligiannidis, A. Bogris, C. Mesaritakis, “Time-Multiplexed Spiking Convolutional Neural Network based on VCSELs for Unsupervised Image Classification”, Applied Sciences, accepted for publication (2021)
  28. G. Sarantoglou, M. Skontranis, A. Bogris, C. Mesaritakis, “Experimental study of Neuromorphic Node based on a Multi- Waveband Emitting two – section Quantum Dot Laser” OSA Photonic Research, doi: 10.1364/PRJ.413371 (2021)
  29. M. Skontranis, G. Sarantoglou, S. Deligiannidis, A. Bogris, C. Mesaritakis, “Unsupervised Image Classification Through Time-Multiplexed Photonic Multi-Layer Spiking Convolutional Neural Network” MDPI Applied Sciences, special issue on Optical Computing, accepted for publication (2021)
  30. Bogris, C. Mesaritakis, Stavros Deligiannidis, Pu Li “All Optical Integrate and Fire Neuromorphic Node based on Single Section Quantum Dot Laser” IEEE Selected Topics in Quantum Electronics, Volume: 27, Issue: 2, (2020)
  31. Mesaritakis, P. Rizomiliotis, M. Akriotou, C. Chaintoutis, A. Fragkos, D. Syvridis “Photonic Pseudo-Random Number Generator for Internet-of-Things Authentication using a Waveguide based Physical Unclonable Function” Arxiv.org (2020)
  32. Deligiannidis, A. Bogris, C. Mesaritakis, Y. Kopsinis, “Compensation of Fiber Nonlinearities in Digital Coherent Systems Leveraging Long Short-Term Memory Neural Networks” ΙΕΕΕ Journal of Lightwave Technology Volume: 38, Issue: 21, pp. 5991-5999 (2020)
  33. Sarantoglou, M. Skontranis, C. Mesaritakis, “All Optical Integrate and Fire Neuromorphic Node based on Single Section Quantum Dot Laser” IEEE Selected Topics in Quantum Electronics, accepted for publication (2019)

 

Conference Proceedings

  1. A. Bogris, K. Sozos, G. Sarantoglou, S. Deligiannidis and C. Mesaritakis, “Neuromorphic computing by means of recurrent spectrum slicing for next generation high baud rate transmission systems,” 2023 IEEE Photonics Society Summer Topicals Meeting Series (SUM), Sicily, Italy, 2023, pp. 1-2, doi: 10.1109/SUM57928.2023.10224454.
  2. K. Sozos, S. Deligiannidis, C. Mesaritakis and A. Bogris, “Unconventional Computing based on Four Wave Mixing in Highly Nonlinear Media,” 2023 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC), Munich, Germany, 2023, pp. 1-1, doi: 10.1109/CLEO/Europe-EQEC57999.2023.10231929.
  3. S. Deligiannidis et al., “Deep-Learning-Based VCSEL Transmitter Emulator,” 2023 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC), Munich, Germany, 2023, pp. 1-1, doi: 10.1109/CLEO/Europe-EQEC57999.2023.10232151.

  4. I. Tsilikas, A. Tsirigotis, S. Deligiannidis, G. N. Tsigaridas, A. Bogris and C. Mesaritakis, “Time-Stretched Imaging Flow Cytometry and Photonic Neuromorphic Processing for Particle Classification,” 2023 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC), Munich, Germany, 2023, pp. 1-1, doi: 10.1109/CLEO/Europe-EQEC57999.2023.10232590.

  5. I. Tsilikas, S. Deligiannidis, A. Tsirigotis, G. N. Tsigaridas, A. Bogris and C. Mesaritakis, “Neuromorphic Camera Assisted High-Flow Imaging Cytometry for Particle Classification,” 2023 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC), Munich, Germany, 2023, pp. 1-1, doi: 10.1109/CLEO/Europe-EQEC57999.2023.10232061.

  6. A. Tsirigotis, I. Tsilikas, K. Sozos, A. Bogris and C. Mesaritakis, “Photonic Neuromorphic Accelerator Combined with an Event-Based Neuromorphic Camera for High-Speed Object Classification,” 2023 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC), Munich, Germany, 2023, pp. 1-1, doi: 10.1109/CLEO/Europe-EQEC57999.2023.10232077.

  7. Smolinski, K. T., Bowden, D. C., Lentas, K., Melis, N. S., Simos, C., Bogris, A., … & Fichtner, A. (2022, December). Exploring DAS as a Tool for Earthquake Monitoring in Urban Environments. In AGU Fall Meeting Abstracts (Vol. 2022, pp. S12E-0190).
  8. Bogris, A., Nikas, T., Simos, C., Simos, I., Lentas, K., Melis, N. S., … & Mesaritakis, C. (2022, December). Microwave Frequency Fiber Interferometry (MFFI): A Promising Technique for Earthquake detection in Commercially Deployed Cables. In AGU Fall Meeting Abstracts (Vol. 2022, pp. S16A-01).
  9. C. Mesaritakis, G. Sarantoglou, A. Bogris “Bayesian Training in Reconfigurable Photonic Neuromorphic Meshes”, (invited) IEEE Workshop on Complexity in Engineering (COMPENG), Florence-Italy (2022)
  10. Bowden, D., Fichtner, A., Nikas, T., Bogris, A., Lentas, K., Simos, C., … & Melis, N. (2022, May). Comparing two fiber-optic sensing systems: Distributed Acoustic Sensing and Direct Transmission. In EGU General Assembly Conference Abstracts (pp. EGU22-11599).
  11. M. Skontranis, G. Sarantoglou, A. Bogris, C. Mesaritakis “Spectro-temporally Multiplexed Reservoir Computing Based on a Multimode Fabry Perot Laser” ECOC Basel – Switzerland 2022
  12. Smolinski, K. T., Bowden, D. C., Lentas, K., Melis, N. S., Simos, C., Bogris, A., … & Fichtner, A. (2022, May). Distributed Acoustic Sensing in the Athens Metropolitan Area: Preliminary Results. In EGU General Assembly Conference Abstracts (pp. EGU22-11864)
  13. A. Bogris, K Sozos, S. Deligiannidis, G. Sarantoglou, C. Mesaritakis, “Machine Learning and Neuromorphic Computing Approaches for the mitigation of transmission impairments in high baud rate transmission systems,” ECOC 2022 (Invited)
  14. A. Tsirigotis, I. Tsilikas, K. Sozos, A. Bogris, C. Mesaritakis “Filter-Based Photonic Reservoir Computing as a key-enabling platform for all-optical high-speed processing of time-stretched images and telecomm data” (invited) SPIE Photonics West, AI and Optical Data Sciences III, San Francisco USA 24-26 February 2022.
  15. A. Bogris, C. Simos, I. Simos, T. Nikas, N. Melis, K. Lentas. C. Mesaritakis, I. Chochliouros, C. Lessi, ” Microwave frequency dissemination systems as sensitive and low-cost interferometers for earthquake detection on commercially deployed fiber cables”, OFC 2022, San Francisco USA
  16. G. Sarantoglou, K. Sozos, T. Kamalakis, C. Mesaritakis, A. Bogris, “Experimental demonstration of an extreme learning machine based on Fabry Perot lasers for parallel neuromorphic processing” OFC 2022, San Francisco USA
  17. K. Sozos, A. Bogris, P. Bienstman, C. Mesaritakis, “Photonic Reservoir Computing based on Optical Filters in a Loop as a High Performance and Low-Power Consumption Equalizer for 100 Gbaud Direct Detection Systems” ECOC, Bordeaux France 2021
  18. A. Bogris, K. Sozos, A. Tsirigotis, C. Mesaritakis, “Neuromorphic Integrated Photonics as Hardware Accelerators for Ultra-high Speed Telecom and Imaging Applications” Photonics in Switching and Computing, OSA Virtual Conference (invited) (2021) 
  19. M. Skontranis, G. Sarantoglou, A. Bogris, C. Mesaritakis, “Photonic Spiking Convolutional Neural Networks for High-Speed Image Processing” (Invited) IEEE Summer Topical Meetings 2021 19- 21 July Virtual Conference (2021)
  20. C. Mesaritakis, K. Sozos, D. Dermanis, A. Bogris, “Spatial Photonic Reservoir Computing based on Non-Linear Phase-to-Amplitude Conversion in Micro-Ring Resonators” OFC USA 2021
  21. K. Sozos, C. Mesaritakis, A. Bogris, “Reservoir Computing based on Mutually Injected Phase Modulated Lasers: A monolithic integration approach suitable for short-reach communication systems”, OFC USA 2021
  22. Y. Hong; S. Deligiannidis; N. Taengnoi; K. Bottrill; N. Thipparapu; Y. Wang; J. Sahu; D. Richardson; C. Mesaritakis; A. Bogris; P. Petropoulos, «Performance-enhanced Amplified O-band WDM
    Transmission using Machine Learning based Equalization» CLEO San Hose-USA (2021),
  23. G. Sarantoglou, M. Skontranis,A. Bogris, C. Mesaritakis, “Resonate and Fire Neuromorphic Node based on two – section Quantum Dot Laser with multi-waveband dynamics”, ECOC-CLEO– Brussels, December (2020)
  24. M. Skontranis, G. Sarantoglou, S. Deligiannidis, A. Bogris, C. Mesaritakis,”Unsupervised Image Classification Through Time-Multiplexed Photonic Multi-Layer Spiking Convolutional Neural Network”, ECOC-CLEO– Brussels, December (2020)
  25. S. Deligiannidis, C. Mesaritakis, A. Bogris, “Performance and Complexity Evaluation of Recurrent Neural Network Models for Fibre Nonlinear Equalization in Digital Coherent Systems”, ECOC – Brussels, December (2020)
  26. Mesaritakis, M. Skontranis, G. Sarantoglou, A. Bogris, “Micro-Ring-Resonator Based Passive Photonic Spike-Time- Dependent-Plasticity Scheme for Unsupervised Learning in Optical Neural Networks” OFC USA – San Diego, March (2020)
  27. Sarantoglou, M. Skontranis, A. Bogris, C. Mesaritakis, “Temporal Resolution Enhancement in Quantum-Dot Laser Neurons due to Ground State Quenching Effects” OFC USA – San Diego, March (2020)
  28. A. Bogris “Multi-mode lasers as potential machines for reservoir computing of enhanced power” ERC International Workshop – Invited – Photonic Reservoir Computing and Information Processing in Complex Networks, Trento-Italy (2019)
  29. Mesaritakis “Passive Photonic Components as Building Blocks for Ultra-Fast Reservoir Computing and as Photonic Spike Dependent Plasticity Enabling Structures” ERC International Workshop – Invited – Photonic Reservoir Computing and Information Processing in Complex Networks, Trento-Italy (2019)
  30. Mesaritakis, “Photonic Reservoir Computing based on the Random-Interaction of Transverse Optical Modes in Large-Cross Section Waveguides” CLEO/EQEC Europe, Munich-Germany (2019)
  31. Μ. Skontranis, G. Sarantoglou, C. Mesaritakis, “Inhibitory Integrate and Fire Neuron based on Quantum-Dot Intra-Band Transitions in a Semiconductor Laser” CLEO/EQEC Europe, Munich-Germany (2019)
  32. Μ. Skontranis, G. Sarantoglou, C. Mesaritakis, “All-optical Inhibitory Integrate and Fire Neuron based on a Single-Section Quantum-Dot Semiconductor Laser” CLEO USA, San-Diego California USA (2019)

RNCP NEWS

Mimicking the brain (live)

December 19th, 2022|

Mimicking the brain (live)

Season 1, Ep. 6

A special live show brought to you from the Berlin Science Week 2022. A recent study in Communications Engineering presents a photonic setup to create a neural network. Tune in to find out more…

Featuring Charis Mesaritakis (University of the Aegean) and Miranda Vinay (Communications Engineering)

Hosted by Cristiano Matricardi (Nature Communications)

Ref: Sozos, K., Bogris, A., Bienstman, P. et al. High-speed photonic neuromorphic computing using recurrent optical spectrum slicing neural networks. Commun Eng 1, 24 (2022). https://doi.org/10.1038/s44172-022-00024-5

ON YOUR WAVELENGTH – ADONIS BOGRIS, CHARIS MESARITAKIS

November 18th, 2022|

On your wavelength is a (physics) podcast where we discuss a paper published recently in a Nature Portfolio journal.  We invite the author(s) and the editor of the paper for an interview, and explore the story behind the paper, including the conception of the research and aspects that you do not find written in the publication — essentially, an account of the ‘scientific journey’ of the paper until completion. We also go through the editorial process with the editor to give a different point of view and discuss post-publication content (if any) and future outlooks.

During this episode, we will talk about neuromorphic computing, which can be interpreted as biologically inspired computing facilitated by deep learning algorithms. The paper we will discuss brings the implementation of this new way of computing one step closer to real-world devices. The authors use light and a clever filtering system that could allow high-bandwidth communications and high-speed imaging.

Authors: Adonis Bogris, University of West Attica, Greece & Charis Mesaritakis, University of the Aegean, Greece

Editor: Miranda Vinay, Communications Engineering

Host: Cristiano Matricardi, Nature Communications

CONTACT RNCP

Adonis Bogris

abogris@uniwa.gr

Charis Mesaritakis

cmesar@aegean.gr

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    Ag. Spyridonos, 122 43 Egaleo,
    Attica, Greece

    email: Adonis Bogris: abogris@uniwa.gr || Charis Mesaritakis: cmesar@aegean.gr

    web: rncp.eu