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Open AccessJournal ArticleDOI

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

Backpropagation through nonlinear units for the all-optical training of neural networks

TL;DR: In this paper, a pump-probe scheme was proposed for optical backpropagation in neural networks, which can achieve state-of-the-art performance on image classification benchmarks.
Journal ArticleDOI

Robust, efficient, micrometre-scale phase modulators at visible wavelengths

TL;DR: In this article, the authors proposed a visible-spectrum silicon nitride thermo-optic phase modulator based on adiabatic micro-ring resonators that offers at least a one-order-of-magnitude reduction in both the device footprint and power consumption compared with waveguide phase modulators.
Journal ArticleDOI

III–V/Si Hybrid MOS Optical Phase Shifter for Si Photonic Integrated Circuits

TL;DR: In this article, a low-loss III-V/Si hybrid MOS optical phase shifter was proposed for high-speed modulation beyond 100Gb/s using an Al2O3 bonding interface deposited by atomic layer deposition.
Posted Content

Scaling advantages of all-to-all connectivity in physical annealers: the Coherent Ising Machine vs. D-Wave 2000Q

TL;DR: An exponential (e^(−O(N^2))) penalty in performance is demonstrated for the D-wave quantum annealer relative to coherent Ising machines when solving Ising problems on dense graphs, which is attributable to the differences in internal connectivity between the machines.
Journal ArticleDOI

Color-tunable persistent luminescence in 1D zinc–organic halide microcrystals for single-component white light and temperature-gating optical waveguides

Bo Zhou, +1 more
- 25 May 2022 - 
TL;DR: In this article , the first use of metal-organic halide microcrystals as temperature-gating active waveguides with promising implications for high-security information communications and high-resolution micro/nanophotonics is presented.
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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