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Open AccessJournal ArticleDOI

Deep learning with coherent nanophotonic circuits

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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Journal ArticleDOI

Resonant Tunneling Diode Nano-Optoelectronic Excitable Nodes for Neuromorphic Spike-Based Information Processing

TL;DR: In this article , an interconnected nano-optoelectronic spiking artificial neuron emitter-receiver system with low energy consumption and high spiking dynamical responses is proposed.
Journal ArticleDOI

Unitary learning for diffractive deep neural network

TL;DR: A unitary learning avenue on diffractive deep neural network is presented, meeting the physical unitary prior in coherent diffraction, and a compatible condition on how to select the nonlinear activation in complex space is unveiled.
Journal ArticleDOI

Tunable RF-photonic filtering with high out-of-band rejection in silicon

TL;DR: In this article, the authors demonstrate all-silicon RF-photonic multi-pole filters with ∼100 times higher spectral resolution than previously possible in silicon photonics, using engineered Brillouin interactions to access long-lived phonons.
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ROBIN: A Robust Optical Binary Neural Network Accelerator

TL;DR: In this paper, a novel optical-domain BNN accelerator, named ROBIN, is presented, which intelligently integrates heterogeneous microring resonator optical devices with complementary capabilities to efficiently implement the key functionalities in BNNs.
Journal ArticleDOI

A Silicon Photonics Computational Lensless Active-Flat-Optics Imaging System.

TL;DR: This work demonstrates the use of silicon photonics as a viable platform for computational imaging with a prototype lensless imaging device that has 20 sensors and a 45-degree field of view, and is contained within a 2,000 μ m volume.
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.
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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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