Conversion optique-hyperfréquence à ondes progressives dans un matériau polymère électro-optique

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Etude et réalisation d'un composant à base de polymère électro-optique pour la conversion de signal du domaine optique vers le domaine hyperfréquence

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Advanced reservoir computers: analogue autonomous systems and real time control.

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Reservoir computer tout optique

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Reservoir computers optiques

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Photonic Reservoir Computing: an overview

Reservoir Computing is a recently introduced computational tool that overcomes someof the weaknesses of recurrent neural networks[1]. A reservoir computer is composed bya recurrent nonlinear network, which performs feature recognition on the inputs, and alinear output layer. Learning only occurs in the linear layer; a reservoir computer cantherefore use many more nonlinear units in the recurrent layer without becomingunmaneageable. Reservoir computers excel in processing time-dependent signals, withperformances at the state-of-the-art level for various tasks; at the same time, they aregood candidates for hardware implementations.Here we present an overview of our lab‘s activities in the field of physical reservoircomputers. We have built an optoelectronic[2] and a full-optical[3] reservoir computer,both based on time multiplexing, i.e. the idea of using a single nonlinear node and a delayline rather than several physically distinct nonlinear nodes.For the optoelectronic setup we use a Mach-Zehnder modulator as the nonlinear element,while we use a semiconductor optical amplifier (SOA) for the all-optical setup; a fiberspool is used in both cases as the delay line. We show in both cases the performance onbenchmark tasks, which is on par with the one from software reservoirs of comparablesizes.We also show an hardware implementation of the analogh readout layer[4]. For this, weuse a Mach-Zehnder modulator to multiply the reservoir states, encoded as lightintensities, by arbitrary coefficients, and a capacitor to integrate the multiplicated statesand produce the output of the linear layer.Preliminary results show that while we sacrifice some of the performance of our reservoirusing this readout, we also gain a factor of 20 in its operating speed, by removing theneed for postprocessing the reservoir outputs. The main advantage of the analog readouthowever is that it opens the possibility of online training, which we hope that will lead toa further improvement on the reservoir performance and eventually to the first allhardware,self-operating reservoir.References[1]M. Lukosevicius and H. Jaeger, Computer Science Review, 3127–149, 2009.[2]Y. Paquot, F. Duport, A. Smerieri, J. Dambre, B. Schrauwen, M. Haelterman, and S.Massar, Scientific reports, 2, 287, 2012.[3]F. Duport, B. Schneider, A. Smerieri, M. Haelterman, and S. Massar, Optics express,vol. 20, 20,22783–95, 2012.[4]A. Smerieri, F. Duport, Y. Paquot, B. Schrauwen, M. Haelterman, and S. Massar,Proceedings of NIPS, 2012.International Workshop on Soft Robotics and Morphological Computation 2013Oinfo:eu-repo/semantics/publishe

Fully analogue photonic reservoir computer

Introduced a decade ago, reservoir computing is an efficient approach for signal processing. State of the art capabilities have already been demonstrated with both computer simulations and physical implementations. If photonic reservoir computing appears to be promising a solution for ultrafast nontrivial computing, all the implementations presented up to now require digital pre or post processing, which prevents them from exploiting their full potential, in particular in terms of processing speed. We address here the possibility to get rid simultaneously of both digital pre and post processing. The standalone fully analogue reservoir computer resulting from our endeavour is compared to previous experiments and only exhibits rather limited degradation of performances. Our experiment constitutes a proof of concept for standalone physical reservoir computers.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Virtual Optical Reservoir Computing

An original solution of time interleaved reservoirs computers is implemented here in order to benefit from the full bandwidth of optical reservoir computer implementation. The resulting virtual reservoir computers perform as well as classical ones.info:eu-repo/semantics/publishe

Autonomous bio-inspired photonic processor based on reservoir computing paradigm

We study numerically a realistic model of a high-performance power efficient photonic neural network coupled to an analog electronic readout layer in order to demonstrate its potential for high-speed computing with no need for external digital post-processing.info:eu-repo/semantics/publishe

FPGA Implementation of Reservoir Computing with Online Learning

Reservoir Computing is a bio-inspired computing paradigm for processing time dependent signals. The performance of its analogue implementation are comparable to, and sometimes surpass, other state of the art algorithms for tasks such as speech recognition or chaotic time series prediction. However, these implementation present several issues, which we address here by using programmable dedicated electronics in place of a personal computer. We demonstrate a standalone reservoir computer programmed onto a FPGA board and apply it to the real-world task of equalisation of a nonlinear communication channel. The training of the RC is carried out online, as this learning method, on top of being simple to implement, allows the RC to adapt itself to a changing environment, as we show here by equalising a variable communication channel.info:eu-repo/semantics/publishe