Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing.

By Laurent Larger, Miguel C. Soriano, Daniel Brunner, L. Appeltant, José M. Gutiérrez, L. Pesquera, Claudio R. Mirasso, Ingo Fischer. Optics Express, 2012

703 citations recorded by the source corpus.

Many information processing challenges are difficult to solve with traditional Turing or von Neumann approaches. Implementing unconventional computational methods is therefore essential and optics provides promising opportunities. Here we experimentally demonstrate optical information processing using a nonlinear optoelectronic oscillator subject to delayed feedback. We implement a neuro-inspired concept, called Reservoir Computing, proven to possess universal computational capabilities. We particularly exploit the transient response of a complex dynamical system to an input data stream. We employ spoken digit recognition and time series prediction tasks as benchmarks, achieving competitive processing figures of merit.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/368a7958ae2b6b8f30454f4405c0bfa090ec5c22

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Citations in the corpus

Emerging opportunities and challenges for the future of reservoir computing (2024, neutral): “…electronic schemes represented by Boolean logic circuits such as FPGA 63 and ASICs 64 ; Non-Von Neumann electrical reservoir scheme represented by memristor 33 and spintronics 65 devices; And photonic schemes represented by silicon photonics 66 , ﬁ ber optics 29,67,68 and free-space optics 69 .”
Information Processing Capacity of Dynamical Systems (2012, background): “…in artificial intelligence (including the whole field of ‘‘reservoir computing’’ 2–5 as well as its recent implementation using time delay systems 6–8 ), and systems of interacting chemicals, such as those found within a cell, that display intra-cellular control mechanisms in response to external…”
Large-Scale Optical Neural Networks based on Photoelectric Multiplication (2018, methodology): “tible and scalable to arrays of millions. Previous approaches used optoelectronics (photodiodes, lasers, ampliers) both to sum neuron inputs [32, 24] and to generate nonlinearity or spiking dynamics [33, 34, 35, 36, 37]; here, thanks to the optical weight encoding, the synaptic weighting itself is p”
Broadcast and Weight: An Integrated Network For Scalable Photonic Spike Processing (2014, background): “For this reason, many attempts to leverage the capabilities of optics avoid a digital electronic computing paradigm altogether and instead target specialized tasks, including A/D conversion [41], amoeba-inspired processing in quantum dots [42], and reservoir computing [43], [44].”
Complex photonics: Dynamics and applications of delay-coupled semiconductors lasers (2013, methodology): “These similarities have also initiated the development of bioinspired photonic information processing applications (Appeltant et al., 2011; Larger et al., 2012).”
Recent Advances in Physical Reservoir Computing: A Review (2018, methodology): “Inthe first experimental demonstrations (Larger et al., 2012; Paquot et al., 2012), the feedback loop was optoelectronic.”
Reservoir Computing Trends (2012, background): “Potential and functioning examples include analog electronics [40,39], randomly crystallized nonlinear electronic networks [44], opto-electronic [26,36] and optical [47] systems, or just a bucket filled with water [12].”