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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

Ultracompact optical switch using a single semisymmetric Fano nanobeam cavity.

TL;DR: The proposed PCNC switch shows great potential in various optical systems on chip to increase the integration and reduce energy consumption and the ultracompact footprint of 14µm2 in the core structure.
Journal ArticleDOI

Collective and synchronous dynamics of photonic spiking neurons

TL;DR: The experimental results show that the effective change causes spontaneous modification of the spiking modes and firing rates of clustered neurons, and such collective dynamics can be utilized to realize efficient heuristics for solving NP-hard combinatorial optimization problems.
Posted Content

Harnessing Optoelectronic Noises in a Photonic Generative Network.

TL;DR: In this article, a photonic generative network (GAN) is proposed to generate a handwritten number ("7") in experiments and full ten digits in simulation using a generator derived from the amplified spontaneous emission noise.
Proceedings ArticleDOI

Photonic Tensor Core with Photonic Compute-in-Memory

TL;DR: In this paper , a photonic tensor core based on a silicon photonics dot-product engine was demonstrated, with the highest throughput density to date of 3.8 MAC/s/mm2.
Journal ArticleDOI

Towards On-Chip Self-Referenced Frequency-Comb Sources Based on Semiconductor Mode-Locked Lasers.

TL;DR: An architecture with a semiconductor mode-locked laser at the heart of the system and subsequent supercontinuum generation and carrier-envelope offset detection and stabilization in nonlinear integrated optics is placed emphasis.
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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