| Authors: | A. Katumba, P. Bienstman, J. Dambre | | Title: | Photonic reservoir computing approaches to nanoscale computation | | Format: | International Conference Proceedings | | Publication date: | 9/2015 | | Journal/Conference/Book: | International Conference on Nanoscale Computing and Communication (NANOCOM
| | Editor/Publisher: | ACM New York, NY, USA, | | Location: | Boston, United States | | DOI: | 10.1145/2800795.2800827 | | Citations: | 4 (Dimensions.ai - last update: 24/11/2024)
1 (OpenCitations - last update: 27/6/2024)
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Abstract
This material is based on work in progress. Reservoir computing, originally a training technique for recurrent neural networks, exploits the computation that naturally occurs in physical dynamical systems. Reservoir computing with integrated nanophotonics potentially offers low-power, high-bandwidth signal processing for telecommunication applications. We present our recent results for optical signal regeneration. Our simulations show that a small-scale low-power integrated photonic reservoir achieves state-of-the-art performance for regenerating optical signals that have traversed fiber lengths of up to 200 km. Related Research Topics
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