| Authors: | S. Sackesyn, C. Ma, A. Katumba, J. Dambre, P. Bienstman | | Title: | A power-efficient architecture for on-chip reservoir computing | | Format: | International Conference Presentation | | Publication date: | 9/2019 | | Journal/Conference/Book: | Artificial Neural Networks and Machine Learning ICANN 2019: Workshop and Special Sessions. ICANN 2019. Lecture Notes in Computer Science
| | Editor/Publisher: | Springer, Cham, | | Volume(Issue): | 11731 p.161-164 | | Location: | Munchen, Germany | | DOI: | 10.1007/978-3-030-30493-5_16 | | Citations: | 7 (Dimensions.ai - last update: 24/11/2024)
1 (OpenCitations - last update: 27/6/2024)
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Abstract
Reservoir computing is a neuromorphic computing paradigm
which is well suited for hardware implementations. In this work, an
enhanced reservoir architecture is introduced as to lower the losses
and improve mixing behaviour in silicon photonic reservoir computing
designs. Related Research Topics
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