Conception and realization of photonic neural networks


One of the challenges faced by the telecommunications industry is to find ways to process information carried by optical telecommunications cables all-optically, i.e., without the need to convert it to the electric domain. A possible way of achieving this goal is to resort to simple optical analogue computers. This would have great advantages in terms of speed and power consumption.


To address this challenge, we use an approach called 'Reservoir Computing' to build simple all-optical processors. This approach allows us to leverage the inherent analogue processing capabilities of non-linear dynamic systems. The architectures used are loosely inspired by artificial neural networks, including some form of “photonic neuron”.

We have obtained promising results for signal classification (for instance wireless or optical channel equalization, speech recognition) and signal forecasting (chaotic time series prediction etc…).


Our team has realized several optical implementations of reservoir computing, including opto-electronic implementations, and all optical implementations based on frequency multiplexing.


In addition to reservoir computing, we are also investigating other architectures to carry out photonic computing, including deep reservoir computers in which several reservoir computers are coupled to each other, extreme learning machines, and Ising machines.


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Funding :




European Training Network (POSTDIGITAL

Updated on March 17, 2022