All-optical reservoir computing

Duport, François; Schneider, Bendix; Smerieri, Anteo; Haelterman, Marc; Massar, Serge

doi:10.1364/oe.20.022783

Cited by 404 publications

(307 citation statements)

References 23 publications

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“…Recently, this high dimensionality of lasers with delayed optical feedback has been exploited for the implementation of optical neural networks [HIL02] and reservoir computing [PAQ12,DUP12,NGU14]. Here, the increase of dimensionality by the feedback leads to the formation of many "virtual network nodes", while only one physical laser node has to be employed, leading to an increase in computational power.…”

Section: Quantum-dot Laser Dynamicsmentioning

confidence: 99%

Nonlinear and Nonequilibrium Dynamics of Quantum-Dot Optoelectronic Devices

Lingnau

2015

Springer Theses

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In this thesis the dynamics and performance of optoelectronic devices based on semiconductor quantum-dots are investigated.In the first part, the dynamics of quantum-dot lasers under external perturbations is discussed. Using a microscopically based balance equation model that incorporates detailed charge-carrier scattering dynamics and the possibility to describe nonequilibrium between intra-band electronic states, the relaxation oscillations of the quantum-dot laser are investigated. Three qualitatively different dynamic regimes are identified in dependence of the scattering rates -the "constantreservoir" regime for slow scattering, the "overdamped" regime, and the "synchronized" regime for high scattering -characterized by a varying degree of nonequilibrium between the quantum-dot and reservoir states.Important differences to conventional lasers are found in the modulation response and the dynamics in optical injection and feedback setups. Common theoretical models and approaches used to describe these applications are shown to yield inaccurate predictions, especially in the "constant-reservoir" and "overdamped" dynamic regimes. An important consequence is that the amplitude-phase coupling in quantum-dot lasers, commonly described by the α-factor, differs from conventional descriptions due to the desynchronization of gain and refractive index. While the α-factor describes bifurcations of fixed points accurately, it fails in describing dynamic solutions and overestimates the extent of complex dynamics. The observed low sensitivity to optical perturbations in quantum-dot lasers can therefore be attributed partly to the charge-carrier nonequilibrium. Three quantum-dot laser models on different levels of sophistication are presented that can accurately describe the quantum-dot nonequilibrium dynamics.In the second part of the thesis, the performance of quantum-dot semiconductor optical amplifiers is investigated, and two types of applications unique to quantum-dots as active medium are discussed. The ground and excited states of the quantum-dots allow an ultra-broad-band amplification of optical data streams. Amplified signals on the ground-state frequencies are shown to generally exhibit higher quality than on the excited state, due to a lower sensitivity of the groundstate to carrier-density variations. Nevertheless the quantum-dot amplifier is found to allow effective amplification on both frequency ranges. Furthermore, a parameter range is identified that allows for a simultaneous amplification of data signals on the ground and excited state in a counter-propagating setup.The long microscopically polarization dephasing times in quantum-dots are found to enable quantum-coherent interactions on a macroscopic scale at room-temperature. By comparison with experiments, the occurrence of Rabi-oscillations by amplification of ultra-short pulses is demonstrated. Quantum-dot based devices could therefore be used for future applications based on quantum-coherent effects.

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Section: Quantum-dot Laser Dynamicsmentioning

confidence: 99%

Nonlinear and Nonequilibrium Dynamics of Quantum-Dot Optoelectronic Devices

Lingnau

2015

Springer Theses

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“…This is particularly attractive when the information is already in the optical domain as in the case of many telecom and image processing applications. Optical reservoirs based on a fibre and one dynamical node [14][15][16][17][18] as well as reservoirs based on ring resonators 19 have been demonstrated. In our own previous work we have shown through reservoir simulations that integrated optical chips with a network of coupled semiconductor optical amplifiers can also be used, with the advantage of a much smaller footprint 20 .…”

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confidence: 99%

Experimental demonstration of reservoir computing on a silicon photonics chip

et al. 2014

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In today's age, companies employ machine learning to extract information from large quantities of data. One of those techniques, reservoir computing (RC), is a decade old and has achieved state-of-the-art performance for processing sequential data. Dedicated hardware realizations of RC could enable speed gains and power savings. Here we propose the first integrated passive silicon photonics reservoir. We demonstrate experimentally and through simulations that, thanks to the RC paradigm, this generic chip can be used to perform arbitrary Boolean logic operations with memory as well as 5-bit header recognition up to 12.5 Gbit s À 1 , without power consumption in the reservoir. It can also perform isolated spoken digit recognition. Our realization exploits optical phase for computing. It is scalable to larger networks and much higher bitrates, up to speeds 4100 Gbit s À 1 . These results pave the way for the application of integrated photonic RC for a wide range of applications.

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“…It is an attractive hardware solution, especially when photonic implementation is concerned, for which high-speed optical telecom devices can potentially provide unprecedented processing speed [6][7][8][9]. Delay dynamics are indeed known as having an infinite-dimensional phase space.…”

Section: B Rc Based On Delay Differential Dynamicsmentioning

confidence: 99%

“…Writing a convolution product for the reservoir dynamics indeed provides a more accurate description compared to previous work. In the past, analytical descriptions of the virtual spatial coupling in delay dynamics were presented either through the approximation provided by a Euler discrete integration time step of the continuous dynamics [5] or, roughly, by neglecting the temporal nearest-neighbor coupling provided by the impulse response function (coupling was then induced through the hypothesis of an asynchronous configuration of the cyclic injected TDM samples with respect to the delay [7,8]). Equation (6) provides a rigorous analytical description of the delay dynamics in a mathematical form that exhibits a close analogy to the original ESN model (recalled in Sec.…”

Section: B Rc Based On Delay Differential Dynamicsmentioning

confidence: 99%

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Reservoir Computing Speeds Up

Soriano

2017

Physics

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Reservoir computing, originally referred to as an echo state network or a liquid state machine, is a braininspired paradigm for processing temporal information. It involves learning a "read-out" interpretation for nonlinear transients developed by high-dimensional dynamics when the latter is excited by the information signal to be processed. This novel computational paradigm is derived from recurrent neural network and machine learning techniques. It has recently been implemented in photonic hardware for a dynamical system, which opens the path to ultrafast brain-inspired computing. We report on a novel implementation involving an electro-optic phase-delay dynamics designed with off-the-shelf optoelectronic telecom devices, thus providing the targeted wide bandwidth. Computational efficiency is demonstrated experimentally with speech-recognition tasks. State-of-the-art speed performances reach one million words per second, with very low word error rate. Additionally, to record speed processing, our investigations have revealed computing-efficiency improvements through yet-unexplored temporalinformation-processing techniques, such as simultaneous multisample injection and pitched sampling at the read-out compared to information "write-in".

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All-optical reservoir computing

Cited by 404 publications

References 23 publications

Nonlinear and Nonequilibrium Dynamics of Quantum-Dot Optoelectronic Devices

Nonlinear and Nonequilibrium Dynamics of Quantum-Dot Optoelectronic Devices

Experimental demonstration of reservoir computing on a silicon photonics chip

Reservoir Computing Speeds Up

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