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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

Photonic integrated field-programmable disk array signal processor.

TL;DR: A scalable photonic field-programmable disk array (FPDA) signal processor that is field programmable using arrays of microdisk resonators is proposed.
Journal ArticleDOI

Recent Progress in Transistor-Based Optoelectronic Synapses: From Neuromorphic Computing to Artificial Sensory System

TL;DR: The recent progresses in transistor‐based optoelectronic synapses for artificial intelligent system are reviewed and their device architecture, neuromorphic operational mechanisms, manufacturing methodologies, and advanced applications for Artificial intelligent computing and visual perception systems are focused.
Journal ArticleDOI

Demonstration of chip-based coupled degenerate optical parametric oscillators for realizing a nanophotonic spin-glass.

TL;DR: The authors exploit χ nonlinearity in SiN to demonstrate on-chip phase-tunable coupling between two DOPO based Ising nodes, which can be deterministically achieved at a fast regeneration speed of 400 kHz with a large phase tolerance.
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Misalignment Resilient Diffractive Optical Networks

TL;DR: This work introduces and experimentally demonstrates a new training scheme that significantly increases the robustness of diffractive networks against 3D misalignments and fabrication tolerances in the physical implementation of a trained diffractive network.
Journal ArticleDOI

Emergent nonlinear phenomena in a driven dissipative photonic dimer

TL;DR: In this article, a pair of photonic integrated Kerr micro-resonators (dimer) is shown to exhibit emergent nonlinear phenomena, such as spontaneous symmetry breaking and spontaneous symmetry hopping.
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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