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Open AccessJournal ArticleDOI

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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Citations
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Proceedings ArticleDOI

Logic synthesis for energy-efficient photonic integrated circuits

TL;DR: Two optimization techniques based on binary decision diagram, combiner elimination and coupler assignment, are proposed to improve the power efficiency for PICs to greatly reduce the optical power depletion and facilitate large-scale on-chip optical computation.
Journal ArticleDOI

Femto-Joule All-Optical Switching Using Epsilon-Near-Zero High-Mobility Conductive Oxide

TL;DR: In this article, the authors proposed a femto-joule level all-optical switch (AOS) using hybrid plasmonic-silicon waveguides driven by high mobility transparent conductive oxides (HMTCOs) such as cadmium oxide.
Journal ArticleDOI

Channel response-aware photonic neural network accelerators for high-speed inference through bandwidth-limited optics.

TL;DR: A novel channel response-aware (CRA) DL architecture that can address the implementation challenges of high-speed compute rates on bandwidth-limited photonic devices by incorporating their frequency response into the training procedure is presented.
Peer ReviewDOI

Advances in lithium niobate photonics: development status and perspectives

TL;DR: Lithium niobate (LN) has experienced significant developments during past decades due to its versatile properties, especially its large electro-optic (EO) coefficient as discussed by the authors .
Journal ArticleDOI

Scalable spin-glass optical simulator

TL;DR: In this paper, an optical scalable spin-glass simulator based on spatial light modulation and multiple light scattering is proposed and realized, which can accelerate the computation of the ground state of large spin networks with all-to-all random couplings.
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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