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Deep learning with coherent nanophotonic circuits

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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

Neuromorphic encoding of image pixel data into rate-coded optical spike trains with a photonic VCSEL-neuron

TL;DR: This work experimentally demonstrates encoding of digital image data into continuous, rate-coded, up to GHz-speed optical spike trains with a VCSEL-based photonic spiking neuron, making the system compatible with current optical network and data center technologies.
Journal ArticleDOI

Scalable analysis for arbitrary photonic integrated waveguide meshes

TL;DR: A scalable method based on mathematical induction that allows one to obtain the scattering matrix of any 2D integrated photonic waveguide mesh circuit composed of an arbitrary number of cells and which is easily programmable is reported.
Journal ArticleDOI

Electronically Programmable Photonic Molecule

TL;DR: In this paper, a photonic molecule with two distinct energy levels and control it by external microwave excitation is demonstrated, including microwave induced photonic Autler-Townes splitting, Stark shift, Rabi oscillation and Ramsey interference.
Journal ArticleDOI

Inverse design of an integrated-nanophotonics optical neural network

TL;DR: An optical neural network framework based on optical scattering units is constructed by introducing “Kernel Matrix”, which can achieve 97.1% accuracy on the classic image classification dataset MNIST.
Journal ArticleDOI

Hybrid optoelectronic synaptic functionality realized with ion gel-modulated In2O3 phototransistors

TL;DR: IoT gel-modulated synaptic transistors with solution-processed In2O3 semiconductors with hybrid optoelectronic pulses demonstrate good electrical performance, and a dynamic logic function was demonstrated by applying spatiotemporally related hybridoptoelectronics pulses.
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.
Journal ArticleDOI

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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