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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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Proceedings ArticleDOI

Training Noise-Resilient Recurrent Photonic Networks for Financial Time Series Analysis

TL;DR: In this article, the authors proposed a noise-aware approach for training neural networks realized on photonic hardware, which can alleviate some of the limitations that hinders its application, including the need to re-train DL models in order to be compliant with the underlying hardware architecture, as well as the existence of various noise sources.
Journal ArticleDOI

On-chip silicon shallowly etched TM 0 -to-TM 1 mode-order converter with high conversion efficiency and low modal crosstalk

TL;DR: In this paper, the authors proposed an on-chip silicon device with shallowly etched rectangular slots on the top surface of silicon nanowire for mode-division-multiplexing (MDM) transmission.
Journal ArticleDOI

Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks

TL;DR: In this paper , a self-monitored all-optical neural network was proposed for object classification and semantic segmentation tasks, which achieved a high accuracy of 97.3%.
Proceedings ArticleDOI

Silicon photonics integration technologies for future computing systems

TL;DR: Two examples of integrated photonics technology are discussed; integrated photonic non-volatile optical weights and a photonicNonvolatile memory based analog accelerator for the inference and training of deep neural networks.
Posted Content

Analog Computing with Metatronic Circuits.

TL;DR: A nanophotonic platform based on epsilon-near-zero materials capable of solving in the analog domain partial differential equations (PDE) and the possibility of implementing the proposed nano-optic processor using CMOS-compatible indium-tin-oxide, whose optical properties can be tuned by carrier injection to obtain programmability at high speeds and low energy requirements is explored.
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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