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Open AccessJournal ArticleDOI

Deep learning with coherent nanophotonic circuits

TLDR
A new architecture for a fully optical neural network is demonstrated that enables a computational speed enhancement of at least two orders of magnitude and three order of magnitude in power efficiency over state-of-the-art electronics.
Abstract
Artificial Neural Networks have dramatically improved performance for many machine learning tasks. We demonstrate a new architecture for a fully optical neural network that enables a computational speed enhancement of at least two orders of magnitude and three orders of magnitude in power efficiency over state-of-the-art electronics.

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Citations
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Journal ArticleDOI

Mode-field switching of nanolasers

TL;DR: In this paper, mode-field switching was used to enable the control of the laser operation via the modulation of the electromagnetic field, which can be implemented in every platform displaying coupled and tuneable resonances.
Journal ArticleDOI

Performing calculus with epsilon-near-zero metamaterials

TL;DR: This work introduces the concept of an epsilon-near-zero (ENZ) metamaterial processing unit (MPU) that performs differentiation and integration on analog signals to achieve extreme miniaturization at the subwavelength scale by generating desired dispersions of the ENZ meetingamaterials with photonic doping.
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Numerical simulation of an InP photonic integrated cross-connect for deep neural networks on chip

TL;DR: The analysis of the prediction error as a function of the optical crosstalk per layer suggests that the first layer is essential to the final accuracy, and the ultimate accuracy shows a quasi-linear dependence between the prediction accuracy and the errors per layer.
Journal ArticleDOI

Optical actuation of a micromechanical photodiode via the photovoltaic-piezoelectric effect.

TL;DR: In this article, the photovoltaic-piezoelectric effect (PVPZ) has been used to actuate micro/nanomechanical structures fabricated from semiconductor PDEs such as gallium arsenide (GaAs).
Journal ArticleDOI

Modelling domain switching of ferroelectric BaTiO3 integrated in silicon photonic waveguides

TL;DR: In this article, a model to investigate the local change of the refractive index of a ferroelectric material employed as upper cladding of silicon photonic waveguides is presented.
References
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Proceedings Article

ImageNet Classification with Deep Convolutional Neural Networks

TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI

Deep learning

TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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Human-level control through deep reinforcement learning

TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
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Reducing the Dimensionality of Data with Neural Networks

TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
Journal ArticleDOI

Deep learning in neural networks

TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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