Deep learning with coherent nanophotonic circuits
Yichen Shen,Nicholas C. Harris,Scott Skirlo,Dirk Englund,Marin Soljacic +4 more
- Vol. 11, Iss: 7, pp 441-446
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.read more
Citations
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Programmable photonic neural networks combining WDM with coherent linear optics
TL;DR: In this paper , a neuron architecture that combines coherent optics with WDM is presented for a multifunctional programmable neural network platform, which accommodates four different operational modes over the same photonic hardware, supporting multi-layer, convolutional, fully-connected and power saving layers.
Journal ArticleDOI
The challenges of modern computing and new opportunities for optics
TL;DR: In this paper, the challenges of modern computing technologies and potential solutions are briefly explained in Chapter 1, the latest research progresses of analog optical computing are separated into three directions: vector/matrix manipulation, reservoir computing and photonic Ising machine.
Journal ArticleDOI
Fundamental aspects of noise in analog-hardware neural networks
Nadezhda Semenova,Nadezhda Semenova,Xavier Porte,Louis Andreoli,Maxime Jacquot,Laurent Larger,Daniel Brunner +6 more
TL;DR: In this article, the authors investigate the signal-to-noise ratio at the network outputs, which determines the upper limit of computational precision, and find that analog neural networks are surprisingly robust against noisy neurons.
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A Survey on Silicon Photonics for Deep Learning
TL;DR: Deep learning has led to unprecedented successes in solving some very difficult problems in domains such as computer vision, natural language processing, and general pattern recognition as mentioned in this paper. But deep learning has not yet shown great success in solving problems in other domains.
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Scaling up silicon photonic-based accelerators: Challenges and opportunities
TL;DR: In this article , the authors study the energy efficiency of integrated silicon photonic MAC circuits based on Mach-Zehnder modulators and microring resonators, and describe the bounds on energy efficiency and scaling limits for NxN optical networks with today's technology, based on the optical and electrical link budget.
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