Photonic reservoir computing approaches to nanoscale computation
| dc.contributor.author | Katumba, Andrew | |
| dc.contributor.author | Bienstman, Peter | |
| dc.contributor.author | Dambre, Joni | |
| dc.date.accessioned | 2022-12-01T18:25:17Z | |
| dc.date.available | 2022-12-01T18:25:17Z | |
| dc.date.issued | 2015 | |
| dc.description.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 o ers lowpower, high-bandwidth signal processing for telecommunication applications. We present our recent results for optical signal regeneration. Our simulations show that a smallscale low-power integrated photonic reservoir achieves stateof- the-art performance for regenerating optical signals that have traversed ber lengths of up to 200 km. | en_US |
| dc.identifier.citation | Katumba, A., Bienstman, P., & Dambre, J. (2015, September). Photonic reservoir computing approaches to nanoscale computation. In Proceedings of the second annual international conference on nanoscale computing and communication (pp. 1-2). http://dx.doi.org/10.1145/2800795.2800827 | en_US |
| dc.identifier.uri | http://dx.doi.org/10.1145/2800795.2800827 | |
| dc.identifier.uri | https://nru.uncst.go.ug/handle/123456789/5630 | |
| dc.language.iso | en | en_US |
| dc.publisher | Nanoscale computing and communication | en_US |
| dc.subject | Photonic reservoir | en_US |
| dc.subject | Computing approaches | en_US |
| dc.subject | Nanoscale computation | en_US |
| dc.title | Photonic reservoir computing approaches to nanoscale computation | en_US |
| dc.type | Other | en_US |
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