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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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Posted Content

Parallel fault-tolerant programming of an arbitrary feedforward photonic network

TL;DR: A graph-topological approach is introduced that defines the general class of feedforward networks commonly used in such applications and identifies columns of non-interacting nodes that can be adjusted simultaneously and can reduce the programming time by a factor of order $N$ to being proportional to the optical depth (or number of node columns in the device).
Journal ArticleDOI

Living optical random neural network with three dimensional tumor spheroids for cancer morphodynamics

TL;DR: In this article, a random optical learning machine (ROM) was used to detect cancer morphodynamics by laser-induced hyperthermia, quantifies chemotherapy, and cell metabolism, and enables real-time investigation of tumor dynamics.
Posted Content

Antiferromagnetic spatial photonic Ising machine through optoelectronic correlation computing

TL;DR: This work proposes to implement an antiferromagnetic model through optoelectronic correlation computing with SPIM, and exploits the gauge transformation which enables encoding the spins and the interaction strengths in a single phase-only spatial light modulator.
Journal ArticleDOI

Integrated Nonreciprocal Photonic Devices with Dynamic Modulation

TL;DR: This article discusses the requirements for constructing an isolator or circulator using dynamic modulation, and reviews a number of different isolator and circulator architectures, including waveguide and resonant devices, and describes their underlying operating principles.
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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