Tutorial: Photonic neural networks in delay systems
Brunner, D.; Penkovsky, B.; Marquez, A.; Jacquot, M.; Fischer, I.; Larger, L.
Journal of Applied Physics 124, 152004 (1-14) (2018)
Photonic delay systems have revolutionized the hardware implementation of Recurrent Neural Networks and Reservoir Computing in particular. The fundamental principles of Reservoir Computing strongly facilitate a realization in such complex analog systems. Especially delay systems, which potentially provide large numbers of degrees of freedom even in simple architectures, can efﬁciently be exploited for information processing. The numerous demonstrations of their performance led to a revival of photonic Artiﬁcial Neural Network. Today, an astonishing variety of physical substrates, implementation techniques as well as network architectures based on this approach have been successfully employed. Important fundamental aspects of analog hardware Artiﬁcial Neural Networks have been investigated, and multiple high-performance applications have been demonstrated. Here, we introduce and explain the most relevant aspects of Artiﬁcial Neural Networks and delay systems, the seminal experimental demonstrations of Reservoir Computing in photonic delay systems, plus the most recent and advanced realizations.