Freiberger, MatthiasSackesyn, StijnMa, ChonghuaiKatumba, AndrewBienstman, PeterDambre, Joni2022-11-292022-11-292019Freiberger, M., Sackesyn, S., Ma, C., Katumba, A., Bienstman, P., & Dambre, J. (2019). Improving time series recognition and prediction with networks and ensembles of passive photonic reservoirs. IEEE Journal of Selected Topics in Quantum Electronics, 26(1), 1-11.https://ieeexplore.ieee.org/abstract/document/8772153/https://nru.uncst.go.ug/handle/123456789/5537As the performance increase of traditional Von- Neumann computing attenuates, new approaches to computing need to be found. A promising approach for low-power computing at high bitrates is integrated photonic reservoir computing. In the past though, the feasible reservoir size and computational power of integrated photonic reservoirs have been limited by hardware constraints. An alternative solution to building larger reservoirs is the combination of several small reservoirs to match or exceed the performance of a single bigger one. This work summarizes our efforts to increase the available computational power by combining multiple reservoirs into a single computing architecture. We investigate several possible combination techniques and evaluate their performance using the classic XOR and header recognition tasks as well as the well-known Santa Fe chaotic laser prediction task. Our findings suggest that a new paradigm of feeding a reservoir’s output into the readout structure of the next one shows consistently good results for various tasks as well as for both electrical and optical readouts and coupling schemes.enintegrated photonic reservoir computingdeep reservoir computingScalable reservoir computingUnconventional computingNeuro-inspired computingNeuromorphic computing.Article