Photonic reservoir computing: A new approach to optical information processing

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 ha...

Full description

Saved in:
Bibliographic Details
Main Authors: Vandoorne, K, Fiers, M, Verstraeten, D, Schrauwen, B, Dambre, J, Bienstman, P
Format: Conference Proceeding
Language:English
Subjects:
High speed optical techniques
Information processing
integrated optics
Machine learning
Neural networks
Nonlinear optics
Optical computing
optical neural networks
Pattern classification
photonic reservoir computing
Reservoirs
Semiconductor optical amplifiers
Speech
speech recognition
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2162-7339
DOI:10.1109/ICTON.2010.5548990