Online Training of an Opto-Electronic Reservoir Computer Applied to Real-Time Channel Equalization

Antonik, Piotr; Duport, François; Hermans, Michiel; Smerieri, Anteo; Haelterman, Marc; Massar, Serge

doi:10.1109/tnnls.2016.2598655

Cited by 75 publications

(53 citation statements)

References 36 publications

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“…It has also been experimentally demonstrated in a wide variety of systems, benefiting from the fact that the dynamical system need not be trained. Successful demonstrations include systems of dissociated neural cell cultures [7], a bucket of water [8] and field programmable gate arrays (FPGAs) [9].…”

Section: Introductionmentioning

confidence: 99%

Multiplexed networks: reservoir computing with virtual and real nodes

Röhm

Lüdge

2018

J. Phys. Commun.

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The reservoir computing scheme is a machine learning mechanism which utilizes the naturally occurring computational capabilities of dynamical systems. One important subset of systems that has proven powerful both in experiments and theory are delay-systems. In this work, we investigate the reservoir computing performance of hybrid network-delay systems systematically by evaluating the NARMA10 and the Sante Fe task for varying system parameters. We construct 'multiplexed networks' that can be seen as intermediate steps on the scale from classical networks to the 'virtual networks' of delay systems. We find that the delay approach can be extended to the network case without loss of computational power, enabling the construction of faster reservoir computing systems.

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Section: Introductionmentioning

confidence: 99%

Multiplexed networks: reservoir computing with virtual and real nodes

Röhm

Lüdge

2018

J. Phys. Commun.

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“…In order to keep the experiment simple, we used as reservoir the opto-electronic delay system introduced in [7][8][9], that has shown state-of-the-art results on several benchmark tasks and is fairly easy to operate. The coupling of an FPGA board to an optoelectronic reservoir was already reported in [31], where the capacity to compute the output in real time was used to solve tasks that change in time. Note however that the FPGA design, i.e.…”

Section: Introductionmentioning

confidence: 99%

Brain-Inspired Photonic Signal Processor for Generating Periodic Patterns and Emulating Chaotic Systems

2017

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Reservoir computing is a bio-inspired computing paradigm for processing time-dependent signals. Its hardware implementations have received much attention because of their simplicity and remarkable performance on a series of benchmark tasks. In previous experiments the output was uncoupled from the system and in most cases simply computed offline on a post-processing computer. However, numerical investigations have shown that feeding the output back into the reservoir would open the possibility of long-horizon time series forecasting. Here we present a photonic reservoir computer with output feedback, and demonstrate its capacity to generate periodic time series and to emulate chaotic systems. We study in detail the effect of experimental noise on system performance. In the case of chaotic systems, this leads us to introduce several metrics, based on standard signal processing techniques, to evaluate the quality of the emulation. Our work significantly enlarges the range of tasks that can be solved by hardware reservoir computers, and therefore the range of applications they could potentially tackle. It also raises novel questions in nonlinear dynamics and chaos theory.

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“…In recent years, implementations of feed-forward and recurrent NNs based on extreme learning machines (ELM) 9,10 and reservoir computing (RC) approaches 11,12,13,14 have been presented in optoelectronic 15,16,17,18 and photonic 19,20,21,22,23,24,25 hardware. These implementations were in some cases assisted by field programmable gate array (FPGA) modules 25,26 . So far, they have only been employed for standard benchmark tasks arXiv:1710.01107v4 2 such as pattern classification, speech recognition, nonlinear time series prediction and wireless channel equalization.Evolving these hardware implementations to minimal conceptual complexity and to maximal speeds would enable to address signal processing tasks in critical technological fields.…”

mentioning

confidence: 99%

Photonic machine learning implementation for signal recovery in optical communications

Argyris

Bueno

Fischer

2018

Sci Rep

148

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Machine learning techniques have proven very efficient in assorted classification tasks. Nevertheless, processing time-dependent high-speed signals can turn into an extremely challenging task, especially when these signals have been nonlinearly distorted. Recently, analogue hardware concepts using nonlinear transient responses have been gaining significant interest for fast information processing. Here, we introduce a simplified photonic reservoir computing scheme for data classification of severely distorted optical communication signals after extended fibre transmission. To this end, we convert the direct bit detection process into a pattern recognition problem. Using an experimental implementation of our photonic reservoir computer, we demonstrate an improvement in bit-error-rate by two orders of magnitude, compared to directly classifying the transmitted signal. This improvement corresponds to an extension of the communication range by over 75%. While we do not yet reach full real-time post-processing at telecom rates, we discuss how future designs might close the gap.

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Online Training of an Opto-Electronic Reservoir Computer Applied to Real-Time Channel Equalization

Cited by 75 publications

References 36 publications

Multiplexed networks: reservoir computing with virtual and real nodes

Multiplexed networks: reservoir computing with virtual and real nodes

Brain-Inspired Photonic Signal Processor for Generating Periodic Patterns and Emulating Chaotic Systems

Photonic machine learning implementation for signal recovery in optical communications

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