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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

Silicon Photonics Codesign for Deep Learning

TL;DR: The detailed analysis of a silicon photonic integrated circuit shows that a codesigned implementation based on the decomposition of large matrix-vector multiplication into smaller instances and the use of nonnegative weights could significantly simplify the photonic implementation of the matrix multiplier and allow increased scalability.
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Myths and truths about optical phase change materials: A perspective

TL;DR: In this paper, the authors clarify some commonly held misconceptions about chalcogenide phase change materials and offer a perspective on new research frontiers in the field of PCM.
Journal ArticleDOI

Terahertz Pulse Shaping Using Diffractive Surfaces

TL;DR: A diffractive network is presented, which is used to shape an arbitrary broadband pulse into a desired optical waveform, forming a compact and passive pulse engineering system, and a physical transfer learning approach is presented to illustrate pulse-width tunability.
Journal ArticleDOI

Superconducting optoelectronic loop neurons

TL;DR: An amplifier chain that converts the current pulse generated when a neuron reaches threshold to a voltage pulse sufficient to produce light from a semiconductor diode is described, and it is shown that a synaptic weight can be modified via a superconducting flux-storage loop inductively coupled to the current bias of the synapse.
Journal ArticleDOI

Scaling capacity of fiber-optic transmission systems via silicon photonics

TL;DR: In this article, the authors provide a system perspective and review recent progress in silicon photonics probing all dimensions of light to scale the capacity of fiber-optic networks toward terabits-per-second per optical interface and petabits per-transmission link.
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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