IDEA IMPROVING DATA DECODING IN OPTICAL COMMUNICATION NETWORKS ALL-OPTICALLY USING NEURO-INSPIRED PHOTONIC SYSTEMS

  • I.P.: Miguel C. Soriano, Ingo Fischer, Claudio Mirasso
  • Coordinador: Ingo Fischer
  • Partners: IFISC-CSIC, IFISC-UIB, Universidad de la Laguna
  • Data d'inici: 1 de gener de 2017
  • Data de finalització: 31 de desembre de 2019

Novel technologies related to optical communications, sensing, the Internet of Things (IoT) and artificial intelligence have been generating unique opportunities and potential to enhance our quality-of-life, and to provide new services for our society and economy. However, the perspective to manage and process the dramatically increasing amount of data relies on our ability to handle these data with high-speed, suitable hardware and much improved energy efficiency.
In this project, it is our aim to develop novel all-optical decoding schemes for optical communication networks that are based on neuro-inspired concepts and are able to fulfill the previous requirements.
Excellently performing neuro-inspired concepts and algorithms, in particular related to machine learning, have been developed, but their energy requirements and lack of speed hinder their implementation in a significant number of current and future applications. In particular, this approach faces severe challenges, when trying to apply it in all-optical communication networks.
Hence, in this proposal we follow a different approach, building upon our experience of designing and realizing neuro-inspired information processing systems, mainly in photonic hardware. In contrast to traditional machine learning, we replace the usual structure of a network composed of multiple connected nodes by a simple dynamical system. The latter comprises a nonlinear node subject to delayed feedback, exploiting the dynamical richness of the delay systems for computational purposes. We aim at extending these concepts by introducing novel pre-processing techniques, taking advantage of multilevel systems and applying novel learning concepts adapted to the particular data and processing requirements. To mitigate the risk, our approach could also be applied in the electronic domain after the signal detection.
The guiding principle will be the realization and implementation of data decoding techniques that combine conceptual and hardware simplicity, high-speed, flexibility, energy efficiency and high performance.
Altogether, this project represents an important step towards ultra-fast, energy-efficient data decoding techniques, complementary to standard approaches. It promises the identification of minimum requirements and the implementation of the concept with high performance. Ultimately, it serves a digital society, in which technology is harnessed to improve data handling and processing and to provide new services.

Investigadors

  • Ingo Fischer

    Ingo Fischer

  • Claudio Mirasso

    Claudio Mirasso

  • Apostolos Argyris

    Apostolos Argyris

  • Miguel C. Soriano

    Miguel C. Soriano

  • Julián Bueno

    Julián Bueno

Publicacions recents

Constructive Role of Noise for High-Quality Replication of Chaotic Attractor Dynamics Using a Hardware-Based Reservoir Computer

Estébanez, Irene; Fischer, Ingo; Soriano, Miguel C.
Physical Review Applied 12, 034058 (2019)

A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection

Alfaras, Miquel; Soriano, Miguel C.; Ortín, Silvia
Frontiers in Physics 7, 103 (2019)

Photonic Information Processing

Bueno Moragues, Julián (Supervisors: Fischer, Ingo; Brunner, Daniel)
PhD Thesis (2019)

PAM-4 transmission at 1550 nm using photonic reservoir computing post-processing

Argyris, Apostolos;Bueno, Julián;Fischer, Ingo
IEEE Access 7, 37017-37025 (2019)

Tutorial: Photonic neural networks in delay systems

Brunner, D.; Penkovsky, B.; Marquez, A.; Jacquot, M.; Fischer, I.; Larger, L.
Journal of Applied Physics 124, 152004 (1-14) (2018)

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