Katumba, AndrewFreiberger, MatthiasBienstman, PeterDambre, Joni2022-11-272022-11-272017Katumba, A., Freiberger, M., Bienstman, P., & Dambre, J. (2017). A multiple-input strategy to efficient integrated photonic reservoir computing. Cognitive Computation, 9(3), 307-314.https://link.springer.com/article/10.1007/s12559-017-9465-5https://nru.uncst.go.ug/handle/123456789/5482Photonic reservoir computing has evolved into a viable contender for the next generation of analog computing platforms as industry looks beyond standard transistor-based computing architectures. Integrated photonics reservoir computing, particularly on the Silicon-on-Insulator platform, presents a CMOS-compatible, wide-bandwidth, parallel platform for implementation of optical reservoirs. A number of demonstrations of the applicability of this platform for processing optical telecommunications signals have been made in the recent past. In this work, we take it a stage further by performing an architectural search for designs that yield the best performance while maintaining power efficiency.enReservoir ComputingIntegrated Pho- tonicsPhotonic Reservoir ComputingReservoir ArchitecturesArticle