Nonlinear photonic dynamical systems for unconventional computing

Abstract

Driven by the remarkable breakthroughs during the past decade, photonics neural networks have experienced a revival. Here, we provide a general overview of progress over the past decade, and sketch a roadmap of important future developments. We focus on photonic implementations of the reservoir computing machine learning paradigm, which offers a conceptually simple approach that is amenable to hardware implementations. In particular, we provide an overview of photonic reservoir computing implemented via either spatio temporal or delay dynamical systems. Going beyond reservoir computing, we discuss recent advances and future challenges of photonic implementations of deep neural networks, of the quest for learning methods that are hardware-friendly as well as realizing autonomous photonic neural networks, i.e. with minimal digital electronic auxiliary hardware.

Reservoir Computing and Task Performing through Using High-β Lasers with Delayed Optical Feedback

“…nodes with spacing θ and τ = N θ. θ is the node separation which determines the time of the nonlinear node that responds to the time-multiplexed input. In detail, if θ is larger than the intrinsic response time of the reservoir nodes, the latter response has enough time to settle down at a certain state [37]. However, if θ is chosen small in comparison with the response time, the response of each nonlinear node will be always on a transient, with each having a motion coupled to its close neighbors [11].…”

“…However, if θ is chosen small in comparison with the response time, the response of each nonlinear node will be always on a transient, with each having a motion coupled to its close neighbors [11]. In addition, the amplitudes of the mask between neighboring θ are often selected randomly from a uniform distribution [37]. Then, the time-multiplexed input signal with the imprinted mask drives the different virtual nonlinear nodes.…”

Implementation of rock-paper-scissors judgment systems with a Ag2S reservoir

“…A hardware implementation of this reservoir layer using physical phenomena is called a physical reservoir. 8,9) Various physical reservoirs have been proposed, including those using water, 10) electronic circuits, 11) optical elements and devices, [12][13][14] resistive change elements, [15][16][17][18][19] magnetic effect, [20][21][22] mechanical systems 23,24) in the reservoir layer.…”