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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

Monolithic InP optical unitary converter based on multi-plane light conversion.

TL;DR: This work presents the first experimental demonstration of 4×4 OUC monolithically integrated on InP, and applies the concept of multi-plane light conversion and cascaded stages of 4-port multimode interference couplers to achieve reconfigurable 4-mode sorting as well as error-free switching of 40-Gbit/s signal.
Journal ArticleDOI

Near-infrared optical properties and proposed phase-change usefulness of transition metal disulfides

TL;DR: In this article, the complex optical constants for select sulfide TMDs (bulk crystals of MoS2, TiS2 and ZrS2) were measured via spectroscopic ellipsometry in the visible-to-NIR range.
Journal ArticleDOI

Universal programmable photonic architecture for quantum information processing

TL;DR: In this article, a photonic integrated circuit architecture for a quantum programmable gate array (QPGA) capable of preparing arbitrary quantum states and operators is presented, which consists of a lattice of phase-modulated Mach-Zehnder interferometers and embedded quantum emitters.
Journal ArticleDOI

Materials for emergent silicon-integrated optical computing

TL;DR: In this paper, the authors focus on oxides showing a strong linear electro-optic effect that can also be integrated with Si, thus capitalizing on new materials to enable the devices and circuit architectures that exploit shifting computational machine learning paradigms, while leveraging current manufacturing infrastructure.
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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