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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

III-V/Silicon hybrid nonlinear nanophotonics in the context of on-chip optical signal processing and analog computing

TL;DR: In this article, the application of hybrid photonic integration technology for all-optical signal processing is discussed and an example of a future nonlinear integrated circuit based on this technology is presented.
Journal ArticleDOI

Photonics enabled intelligence system to identify SARS-CoV 2 mutations

TL;DR: In this article , the authors proposed a framework of photonics based on AI for identifying and sorting SARS-CoV 2 mutations, and compared the omicron mutation with other variants.
Peer ReviewDOI

Universal Linear Optics Revisited: New Perspectives for Neuromorphic Computing With Silicon Photonics

TL;DR: In this paper , the authors present a framework for matrix vector multiplications required by neuromorphic silicon photonic circuits, supporting high-speed and high-accuracy neural network (NN) inference, highspeed tiled matrix multiplication, and programmable photonic NNs.
Journal ArticleDOI

Anisotropic Radiation in Heterostructured “Emitter in a Cavity” Nanowire

TL;DR: In this article , the photoluminescence signal tends to couple into the nanowire cavity acting as a Fabry-Perot resonator, while weak radiation propagating perpendicular to the Nanowire axis is registered in the vicinity of each nano-sized disc.
Journal ArticleDOI

Dynamical photon–photon interaction mediated by a quantum emitter

TL;DR: In this article , a quantum emitter is coupled to a nanophoton waveguide to realize a quantum nonlinear interaction between single-photon wavepackets and a second photon mediated by the emitter.
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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