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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

Developing a photonic hardware platform for brain-inspired computing based on 5 × 5 VCSEL arrays

TL;DR: It is found that the investigated array can readily be tuned to the required spectral homogeneity, and as such show that VCSEL arrays based on this technology can act as highly energy efficient and ultra-fast photonic neurons for next generation photonic neural networks.
Journal ArticleDOI

3D printed multimode-splitters for photonic interconnects

TL;DR: In this paper, optical losses and splitting uniformity of 1 to 4, 1 to 9, and 1 to 16 splitters were evaluated at 632 nm, and it was shown that both the uniformity and overall losses depend on the separation between the output waveguides as well as on the hatching distance (surface quality) of the 3D printing process.
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Integrated photonic neural network based on silicon metalines.

TL;DR: The performance of the optical neural network is benchmarked on the prototypical machine learning task of classification of handwritten digits images from the Modified National Institute of Standards and Technology (MNIST) dataset, and an accuracy comparable to the state of the art is achieved.
Journal ArticleDOI

Meta-programmable analog differentiator

TL;DR: In this article , the fundamental ingredient of wave-based signal differentiation, namely zeros of the scattering matrix that lie exactly on the real axis, can be imposed at will and in situ by purposefully perturbing an overmoded random scattering system.
Journal ArticleDOI

Artificial Intelligence in Meta-optics

TL;DR: A comprehensive review of meta-optics and artificial intelligence in synergy can be found in this article , where the authors categorize and discuss the recent developments integrated by these two topics.
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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