All-Optical Reservoir Computing on a Photonic Chip Using Silicon-Based Ring Resonators

Coarer, Florian Denis-le; Sciamanna, Marc; Katumba, Andrew; Freiberger, Matthias; Dambre, Joni; Bienstman, Peter; Rontani, Damien

doi:10.1109/jstqe.2018.2836985

Cited by 70 publications

(29 citation statements)

References 32 publications

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“…At present, the mechanism of the nonlinear activation function unit first converts the optical signal into an electrical signal detection mechanism, and then converts it into an optical signal. In optical reservoir computing [72], the most commonly used optical nonlinear unit is graphene saturable absorber or is implemented based on the two-photon absorption [73], other research on nonlinear is based on the bistable switch and ring-resonators [74], [75]. However, these methods do not reach an ideal expectation in efficiency and speed.…”

Section: B Other Nonlinear Optical Activationmentioning

confidence: 99%

A Review of Optical Neural Networks

et al. 2020

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Optical neural network can process information in parallel by using the technology based on free-space and integrated platform. Over the last half century, the development of integrated circuits has been limited by Moore's law. We know that neural network is based on the digital computer for successive calculation, most of which cannot be made into real-time processing system. Therefore, it is necessary to develop ONN for real-time processing and device miniaturization. In this paper, we review the progress of optical neural networks. Firstly, based on the principle of artificial neural networks, we elaborate the essence of optical matrix multiplier for linear operation. Then we introduce the optical neural network achieved by free-space optical interconnection and waveguide optical interconnection. Finally we talk about the nonlinearity in optical neural networks. With the gradual maturity of nanotechnology and the rapid advancement of silicon photonic integrated circuits, the progress of integrated photonic neural network has been promoted. Therefore, the construction of optical neural network on the future integrated photonic platform has potential application value. INDEX TERMS Optical neural networks, optical waveguides, free-space optical interconnection, optical non-linearities, optical elements, photonic integrated circuits.

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Section: B Other Nonlinear Optical Activationmentioning

confidence: 99%

A Review of Optical Neural Networks

et al. 2020

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“…For example, graphical processing units (GPUs) have demonstrated peak performance with trillions of floating point operations per second (TFLOPS) when performing matrix multiplication, which is several orders of magnitude larger than general-purpose digital processors such as CPUs [1]. Moreover, analog computing has been explored for achieving high performance because it is not limited by the bottlenecks of sequential instruction execution and memory access [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks

Williamson

Hughes

Minkov

et al. 2020

IEEE J. Select. Topics Quantum Electron.

231

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We introduce an electro-optic hardware platform for nonlinear activation functions in optical neural networks. The optical-to-optical nonlinearity operates by converting a small portion of the input optical signal into an analog electric signal, which is used to intensity-modulate the original optical signal with no reduction in processing speed. Our scheme allows for complete nonlinear on-off contrast in transmission at relatively low optical power thresholds and eliminates the requirement of having additional optical sources between each layer of the network. Moreover, the activation function is reconfigurable via electrical bias, allowing it to be programmed or trained to synthesize a variety of nonlinear responses. Using numerical simulations, we demonstrate that this activation function significantly improves the expressiveness of optical neural networks, allowing them to perform well on two benchmark machine learning tasks: learning a multi-input exclusive-OR (XOR) logic function and classification of images of handwritten numbers from the MNIST dataset. The addition of the nonlinear activation function improves test accuracy on the MNIST task from 85% to 94%.

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“…This unique neuron of the system has a particular number of connections deriving from other neurons. The perceptron’s development into OΝNs is the most fundamental scientific field, with many articles having been published with respective materializations [ 20 , 50 , 51 , 52 , 53 ]. An all-optical neural network (AONN) architecture with a hidden layer is presented in Figure 2 [ 54 ].…”

Section: Architecturesmentioning

confidence: 99%

A Comprehensive Survey on Nanophotonic Neural Networks: Architectures, Training Methods, Optimization, and Activations Functions

Demertzis

Papadopoulos

Iliadis

et al. 2022

Sensors

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In the last years, materializations of neuromorphic circuits based on nanophotonic arrangements have been proposed, which contain complete optical circuits, laser, photodetectors, photonic crystals, optical fibers, flat waveguides and other passive optical elements of nanostructured materials, which eliminate the time of simultaneous processing of big groups of data, taking advantage of the quantum perspective, and thus highly increasing the potentials of contemporary intelligent computational systems. This article is an effort to record and study the research that has been conducted concerning the methods of development and materialization of neuromorphic circuits of neural networks of nanophotonic arrangements. In particular, an investigative study of the methods of developing nanophotonic neuromorphic processors, their originality in neuronic architectural structure, their training methods and their optimization was realized along with the study of special issues such as optical activation functions and cost functions. The main contribution of this research work is that it is the first time in the literature that the most well-known architectures, training methods, optimization and activations functions of the nanophotonic networks are presented in a single paper. This study also includes an extensive detailed meta-review analysis of the advantages and disadvantages of nanophotonic networks.

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All-Optical Reservoir Computing on a Photonic Chip Using Silicon-Based Ring Resonators

Cited by 70 publications

References 32 publications

A Review of Optical Neural Networks

A Review of Optical Neural Networks

Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks

A Comprehensive Survey on Nanophotonic Neural Networks: Architectures, Training Methods, Optimization, and Activations Functions

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