| Authors: | F. Laporte, J. Dambre, P. Bienstman | | Title: | Neuromorphic Computing with Signal-Mixing Cavities | | Format: | International Conference Presentation | | Publication date: | 10/2018 | | Journal/Conference/Book: | IEEE International Conference on Rebooting Computing
| | Editor/Publisher: | IEEE, | | Location: | Washington DC, United States | | DOI: | 10.1109/icrc.2018.8638622 | | Citations: | Look up on Google Scholar
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Abstract
We propose a new approach for doing neuromorphic computing on a silicon photonic chip, based on the concept of reservoir computing. The proposed reservoir computer consists of a signal-mixing photonic crystal cavity acting as the reservoir connected to a linear readout layer. The signal mixing cavity has a quarter-stadium shape, which is known to introduce non-trivial mixing of an input wave. This mixing turns out to be very useful in the context of reservoir computing and has been used to tackle several benchmark telecom tasks. We show that the proposed reservoir computer can perform several digital tasks with a very wide region of operation in terms of bitrate, such as up to 6 bit header recognition and performing the XOR between two subsequent bits in a bitstream. Related Research Topics
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