On-chip Passive Photonic Reservoir Computing with Integrated Optical Readout
| dc.contributor.author | Freiberger, Matthias | |
| dc.contributor.author | Katumba, Andrew | |
| dc.contributor.author | Bienstman, Peter | |
| dc.contributor.author | Dambre, Joni | |
| dc.date.accessioned | 2022-11-30T21:07:13Z | |
| dc.date.available | 2022-11-30T21:07:13Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | Photonic reservoir computing is a recent bioinspired paradigm for signal processing. Despite first successes, the paradigm still faces challenges. We address some of these challenges and introduce our approaches to solve them. In detail, we discuss how integrated reservoirs can be scaled up by injecting multiple copies of the input. Further we introduce a new hardware-friendly training method for integrated optical readouts. | en_US |
| dc.identifier.citation | Freiberger, M., Katumba, A., Bienstman, P., & Dambre, J. (2017, November). On-chip passive photonic reservoir computing with integrated optical readout. In 2017 IEEE International Conference on Rebooting Computing (ICRC) (pp. 1-4). IEEE. | en_US |
| dc.identifier.uri | https://ieeexplore.ieee.org/abstract/document/8123673/ | |
| dc.identifier.uri | https://nru.uncst.go.ug/handle/123456789/5588 | |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE International Conference on Rebooting Computing | en_US |
| dc.title | On-chip Passive Photonic Reservoir Computing with Integrated Optical Readout | en_US |
| dc.type | Other | en_US |
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