NDLI: Online Training for High-Performance Analogue Readout Layers in Photonic Reservoir Computers

Content Provider	Springer Nature Link
Author	Antonik, Piotr Haelterman, Marc Massar, Serge
Copyright Year	2017
Abstract	Reservoir computing is a bio-inspired computing paradigm for processing time-dependent signals. The performance of its hardware implementation is comparable to state-of-the-art digital algorithms on a series of benchmark tasks. The major bottleneck of its implementations is the readout layer, based on slow offline post-processing. Few analogue solutions have been proposed, but all suffered from noticeable decrease in performance due to added complexity of the setup. Here, we propose the use of online training to solve these issues. We study the applicability of this method using numerical simulations of an experimentally feasible reservoir computer with an analogue readout layer. We also consider a nonlinear output layer, which would be very difficult to train with traditional methods. We show numerically that online learning allows to circumvent the added complexity of the analogue layer and obtain the same level of performance as with a digital layer. This work paves the way to high-performance fully analogue reservoir computers through the use of online training of the output layers.
Starting Page	297
Ending Page	306
Page Count	10
File Format	PDF
ISSN	18669956
Journal	Cognitive Computation
Volume Number	9
Issue Number	3
e-ISSN	18669964
Language	English
Publisher	Springer US
Publisher Date	2017-03-11
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Reservoir computing Opto-electronics Analogue readout FPGA Online training Neurosciences Computation by Abstract Devices Artificial Intelligence (incl. Robotics) Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition

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