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Deep Photonic Reservoir Computer Based on Frequency Multiplexing with Fully Analog Connection Between Layers
Alessandro Lupo, Enrico Picco, Marina Zajnulina +1
Reservoir computers (RC) are randomized recurrent neural networks well adapted to process time series, performing tasks such as nonlinear distortion compensation or prediction of c…
Photonic Extreme Learning Machine based on frequency multiplexing
Alessandro Lupo, Lorenz Butschek, Serge Massar
The optical domain is a promising field for physical implementation of neural networks, due to the speed and parallelism of optics. Extreme Learning Machines (ELMs) are feed-forwar…
Parallel photonic reservoir computing using frequency multiplexing of neurons
Akram Akrout, Arno Bouwens, François Duport +3
Today's unrelenting increase in demand for information processing creates the need for novel computing concepts. Reservoir computing is such a concept that lends itself particularl…
Embodiment of Learning in Electro-Optical Signal Processors
Michiel Hermans, Piotr Antonik, Marc Haelterman +1
Delay-coupled electro-optical systems have received much attention for their dynamical properties and their potential use in signal processing. In particular it has recently been d…
Online Training of an Opto-Electronic Reservoir Computer Applied to Real-Time Channel Equalisation
Piotr Antonik, François Duport, Michiel Hermans +3
Reservoir Computing is a bio-inspired computing paradigm for processing time dependent signals. The performance of its analogue implementation are comparable to other state of the…
Analog input layer for optical reservoir computers
François Duport, Akram Akrout, Anteo Smerieri +2
Reservoir computing is an information processing technique, derived from the theory of neural networks, which is easy to implement in hardware. Several reservoir computer hardware…