Katumba, AndrewBienstman, PeterDambre, Joni2022-12-012022-12-012015Katumba, 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/10.1145/2800795.2800827http://dx.doi.org/10.1145/2800795.2800827https://nru.uncst.go.ug/handle/123456789/5630This 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.enPhotonic reservoirComputing approachesNanoscale computationOther