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Piotr Antonik

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2 papers

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AAAI Conference 2017 Short Paper

Chaotic Time Series Prediction Using a Photonic Reservoir Computer with Output Feedback

Piotr Antonik
Michiel Hermans
Marc Haelterman
Serge Massar

Reservoir Computing is a bio-inspired computing paradigm for processing time dependent signals (Jaeger andHaas 2004; Maass, Natschlger, and Markram 2002). It canbe easily implemented in hardware. The performance ofthese analogue devices matches digital algorithms on a series of benchmark tasks (see e.g. (Soriano et al. 2015) fora review). Their capacities could be extended by feedingthe output signal back into the reservoir, which would allow them to be applied to various signal generation tasks(Antonik et al. 2016b). In practice, this requires a high-speed readout layer for real-time output computation. Herewe achieve this by means of a field-programmable gate array (FPGA), and demonstrate the first photonic reservoircomputer with output feedback. We test our setup on theMackey-Glass chaotic time series generation task and obtain interesting prediction horizons, comparable to numerical simulations, with ample room for further improvement.Our work thus demonstrates the potential offered by the output feedback and opens a new area of novel applications forphotonic reservoir computing.

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AAAI Conference 2017 Short Paper

Improving Performance of Analogue Readout Layers for Photonic Reservoir Computers with Online Learning

Piotr Antonik
Marc Haelterman
Serge Massar

Reservoir Computing is a bio-inspired computing paradigm for processing time-dependent signals (Jaeger and Haas 2004; Maass, Natschlger, and Markram 2002). The performance of its hardware implementation (see e.g. (Soriano et al. 2015) for a review) is comparable to state-of-the-art digital algorithms on a series of benchmark tasks.The major bottleneck of these implementation is the readout layer, based on slow offline post-processing. Several analogue solutions have been proposed (Smerieri et al. 2012; Duport et al. 2016; Vinckier et al. 2016), but all suffered from noticeable decrease in performance due to added complexity of the setup. Here we propose the online learning approach to solve these issues. We present an experimental reservoir computer with a simple analogue readout layer, based on previous works, and show numerically that online learning allows to disregard the added complexity of an analogue layer and obtain the same level of performance as with a digital layer. This work thus demonstrates that online training allows building high-performance fully-analogue reservoir computers, and represents an important step towards experimental validation of the proposed solution.

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