Project description
Miniature photonic 'brains' restore integrity in optical networks
Optical networks in which information is carried by photons, rather than electrons, have revolutionised information and communications technologies, supporting an explosion of new applications in many fields. Communications capacity has grown exponentially but is facing an important bottleneck due to hardware-related signal degradation. The EU-funded ALPI project is developing optical circuits that mimic neuronal circuits such as those found in the nervous system to overcome this barrier. In contrast to other computational approaches that are associated with high energy consumption, these all-optical novel networks can be trained to do their jobs efficiently, reducing energy consumption and increasing transmission capacity.
Objective
ALPI aims at the integration of a photonic neural network within an optical transceiver to increase the transmission capacity
of the optical link. Based on a deep learning approach, the new compact device provides real time compensation of fiber
nonlinearities which degrade optical signals. In fact, the tremendous growth of transmission bandwidth both in optical
networks as well as in data centers is baffled by the optical fiber nonlinear Shannon capacity limit. Nowadays, computational
intensive approaches based on power hungry software are commonly used to mitigate fiber nonlinearities. Here, we propose
to integrate in the optical link the neuromorphic photonic circuits which we are currently developing in the ERC-AdG
BACKUP project. Specifically, the proposed error-correction circuit implements a small all-optical complex-valued neural
network which is able to recover distortion on the optical transmitted data caused by the Kerr nonlinearities in multiwavelength
optical fibers. Network training is realized by means of efficient gradient-free methods using a properly designed
data-preamble.
A new neuromorphic transceiver demonstrator realized in active hybrid Si/InP technology will be designed, developed and
tested on a 100 Gbps 80 km long optical link with multiple-levels symbols. The integrated neural network will mitigate the
nonlinearities either by precompensation/autoencoding at the transmitter TX side or by data correction at the receiver RX
side or by concurrently acting on both the TX and RX sides. This achievement will bear to the second ALPI’s goal: moving
from the demonstrator to the industrialization of the improved transceiver. For this purposes, patents will be filed and a business plan will be developed in partnership with semiconductor, telecom and IT companies where a path to the commercialization will be
individuated. The foreseen market is the big volume market of optical interconnection in large data centers or metro
networks.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- engineering and technologymaterials engineeringfibers
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationstelecommunications networksoptical networks
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencesphysical sciencesopticsfibre optics
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
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Programme(s)
Funding Scheme
ERC-POC-LS - ERC Proof of Concept Lump Sum PilotHost institution
38122 Trento
Italy