|Authors: ||K.T Vandoorne, M. Fiers, D. Verstraeten, B. Schrauwen, J. Dambre, P. Bienstman|
|Title: ||Photonic Reservoir Computing: a New Approach to Optical Information Processing|
|Format: ||International Conference Proceedings|
|Publication date: ||6/2010|
|Journal/Conference/Book: ||International Conference on Transparent Optical Networks 2010 (ICTON)
|Volume(Issue): || p.Th.A4.3|
|Location: ||Munich, Germany|
|Citations: ||7 (Dimensions.ai - last update: 29/1/2023)|
1 (OpenCitations - last update: 3/5/2023)
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Despite ever increasing computational power, recognition and classification problems remain challenging to
solve. Recently advances have been made by the introduction of the new concept of reservoir computing. This is
a methodology coming from the field of machine learning and neural networks and has been successfully used in
several pattern classification problems, like speech and image recognition. The implementations have so far been
in software, limiting their speed and power efficiency. Photonics could be an excellent platform for a hardware
implementation of this concept because of its inherent parallelism and unique nonlinear behaviour.
We propose using a network of coupled Semiconductor Optical Amplifiers (SOA) and show in simulation that
it could be used as a reservoir by comparing it on a benchmark speech recognition task to conventional software
implementations. In spite of several differences, they perform as good as or better than conventional
implementations. Moreover, a photonic implementation offers the promise of massively parallel information
processing with low power and high speed.
We will also address the role phase plays on the reservoir performance.
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