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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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Dissertation

Silicon Waveguide Integrated Nanoplasmonics for Optoelectronic and Sensing Applications

Che Chen
TL;DR: Li et al. as mentioned in this paper presented a Ph.D. on Electrical/Computer Engineering at the University of Minnesota, Bloomington, MN. Advisor: Mo Li. August 2018. 1 computer file (PDF); x, 112 pages.
Journal ArticleDOI

Orthogonality of diffractive deep neural network

TL;DR: In this paper , the authors show that the inner product of any two optical fields in D2NN is invariant and the D2N acts as a unitary transformation for optical fields.
Peer ReviewDOI

Universal Linear Optics for Ultra-Fast Neuromorphic Silicon Photonics Towards Fj/MAC and TMAC/sec/mm2 Engines

TL;DR: In this article , the performance of state-of-the-art neuromorphic photonic accelerators is reviewed, summarizing the impact of the circuit architecture and employed weight technology on the system credentials in terms of scalability, energy and footprint-efficiency.
Journal ArticleDOI

Electro-optical logic using dual-nanobeam Mach-Zehnder interferometer switches.

TL;DR: This paper investigates the dual-nanobeam (NB) based MZI 2 × 2 switches with much smaller footprint for realizing electro-optical logic circuits and shows that the NB MZI is another promising candidate for electronic-photonic digital computing.
Journal ArticleDOI

Developing of a photonic hardware platform for brain-inspired computing based on $5\times5$ VCSEL arrays

TL;DR: In this paper, a nanophotonic hardware platform of fast and energy-efficient photonic neurons via arrays of high-quality vertical cavity surface emitting lasers (VCSELs) 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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