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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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Journal ArticleDOI

Coherent Photonic Crossbar Arrays for Large-Scale Matrix-Matrix Multiplication

TL;DR: In this paper , a hybrid photonic-electronic computing architecture was proposed to perform large-scale coherent matrix-matrix multiplication, bypassing the requirements of high-speed electronic readout and frequent reprogramming of photonic weights.
Journal ArticleDOI

Reversibly Programmable Photonics via Responsive Polyelectrolyte Multilayer Cladding

TL;DR: In this paper, a reversible programmable photonic integrated circuits (PIC) using a responsive polyelectrolyte multilayer (PEM) cladding is presented, where reversible de-swelling of PEMs by consecutive exposure to acidic and neutral pH solutions yields highly contrasting refractive index changes in the dry film.
Journal ArticleDOI

Excitability in an all-fiber laser with a saturable absorber section

TL;DR: In this paper, the authors report the first demonstration of excitability in an all-fiber laser with gain and absorber sections, including a threshold-based excitable response and a decreasing reaction delay between input pulse and excitatory response with increasing perturbation amplitude.
Journal ArticleDOI

An analog electronic emulator of non-linear dynamics in optical microring resonators

TL;DR: In this paper, an analog electronic emulator that implements dynamics, attempting to reproduce the self-pulsing phenomenon in an optical microresonator, is presented, which can be readily constructed with off-the-shelf components, and is well suited for building complex networks.
Journal ArticleDOI

The Design of a Low-Loss, Fast-Response, Metal Thermo-Optic Phase Shifter Based on Coupled-Mode Theory

TL;DR: In this article , the authors proposed a method to place high-loss materials close to the optical waveguide while maintaining the low loss of the optical device, which ensures the low insertion loss (~0.78 dB) of the phase shifter.
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.
Journal ArticleDOI

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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