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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 Workshop
Publication date: 7/2010
Journal/Conference/Book: Workshop on "Cognitive and neural models for automated processing of speech and text" 2010 (CONAS)
Location: Ghent, Belgium
Citations: Look up on Google Scholar
Download: Download this Publication (97KB) (97KB)


Despite ever increasing computational power, recognition and classification problems remain challenging to solve. Recently, advances have been made by the introduction of the concept of reservoir computing. This is a methodology coming from the field of machine learning and neural networks that has been successfully used in several pattern classification problems, like speech and image recognition. Thus far, most implementations have 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. Moreover, a photonic implementation offers the promise of massively parallel information processing with low power and high speed. 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 to conventional software implementations using a benchmark speech recognition task. In spite of the differences with classical reservoir models, the performance of our photonic reservoir is comparable to that of conventional implementations with the same number of nodes. As our implementation uses coherent light for information processing, we find that phase tuning is crucial to obtain high performance.

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