| Authors: | F. Laporte, J. Dambre, P. Bienstman | | Title: | Reservoir Computing with signal-mixing cavities | | Format: | International Conference Presentation | | Publication date: | 7/2017 | | Journal/Conference/Book: | 19th International Conference on Transparent Optical Networks (ICTON)
(invited)
| | Editor/Publisher: | IEEE, | | Volume(Issue): | p.We.A5.3 | | Location: | Girona, Spain | | DOI: | 10.1109/ICTON.2017.8024990 | | Citations: | 1 (Dimensions.ai - last update: 24/11/2024)
1 (OpenCitations - last update: 27/6/2024)
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Abstract
In an age where we get swamped by big data, new machine learning techniques for efficient high-speed data processing become more important than ever. One of these techniques, known as reservoir computing, is specifically designed for processing time-dependent data. We propose some new ideas for implementing such a reservoir computer on a silicon photonics chip for low-power and high-bandwidth optical communication applications. Our simulations show that this photonic reservoir can for example be used in pattern recognition tasks such as header recognition. Related Research Topics
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