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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

Analyzing and generating multimode optical fields using self-configuring networks

TL;DR: In this paper, a self-configuring network of 2×2 blocks, such as integrated Mach-Zehnder interferometers, can automatically align itself to the optical field by a sequence of simple one-parameter power minimizations when network elements such as phase shifters are adjusted.
Journal ArticleDOI

WDM equipped universal linear optics for programmable neuromorphic photonic processors

TL;DR: A radically new approach for promoting the synergy of WDM with universal linear optics is presented and a new, high-fidelity crossbar-based neuromorphic photonic platform, able to support matmul with multidimensional operands is demonstrated, forming in this way the first WDM-equipped universal linear optical operator.
Journal ArticleDOI

Real-time monitoring and gradient feedback enable accurate trimming of ion-implanted silicon photonic devices.

TL;DR: A highly accurate trimming method combining laser annealing of germanium implanted silicon waveguide and real-time monitoring of device performance is demonstrated.
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Ultra-compact nonvolatile phase shifter based on electrically reprogrammable transparent phase change materials

TL;DR: In this paper , a phase shifting mechanism that exploits the nonvolatile refractive index modulation upon structural phase transition of Sb 2 Se 3 , a bi-state transparent phase change material (PCM), is introduced.
Posted Content

Adiabatic evolution on a spatial-photonic Ising machine.

TL;DR: In this paper, a photonic scheme for combinatorial optimization in analogy with adiabatic quantum algorithms and enforced by optical vector-matrix multiplications and scalable photonic technology is demonstrated.
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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