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
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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.read more
Citations
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Journal ArticleDOI
Optoelectronic Synapse Based on IGZO-Alkylated Graphene Oxide Hybrid Structure
Jia Sun,Jia Sun,Seyong Oh,Yongsuk Choi,Seunghwan Seo,Min Jun Oh,Min Hwan Lee,Won Bo Lee,Pil J. Yoo,Jeong Ho Cho,Jin-Hong Park +10 more
TL;DR: Owing to this enhancement of synaptic operation, the recognition rates for the Modified National Institute of Standards and Technology digit patterns improve from 36% and 49% to 50% and 62% in artificial neural networks using long‐term potentiation/depression characteristics with 20 and 100 weight states, respectively.
Journal ArticleDOI
Large-Scale Optical Neural Networks Based on Photoelectric Multiplication
TL;DR: Simulations of the network using models for digit- and image-classification reveal a "standard quantum limit" for optical neural networks, set by photodetector shot noise, which suggests performance below the thermodynamic limit for digital irreversible computation is theoretically possible in this device.
Journal ArticleDOI
Deep Neural Network Inverse Design of Integrated Photonic Power Splitters.
Mohammad H. Tahersima,Keisuke Kojima,Toshiaki Koike-Akino,Devesh K. Jha,Bingnan Wang,Chungwei Lin,Kieran Parsons +6 more
TL;DR: This work uses deep learning to predict optical response of artificially engineered nanophotonic devices and paves the way for rapid design of integrated photonic components relying on complex nanostructures.
Journal ArticleDOI
In-memory computing on a photonic platform
Carlos Ríos,Nathan Youngblood,Zengguang Cheng,Manuel Le Gallo,Wolfram H. P. Pernice,C. David Wright,Abu Sebastian,Harish Bhaskaran +7 more
TL;DR: In this paper, the authors combine integrated optics with collocated data storage and processing to enable all-photonic in-memory computations, which can leverage the increased speed and bandwidth potential of the optical domain and remove the need for electro-optical conversions.
Journal ArticleDOI
Continuous-variable quantum neural networks
Nathan Killoran,Thomas R. Bromley,Juan Miguel Arrazola,Maria Schuld,Nicolás Quesada,Seth Lloyd +5 more
TL;DR: In this paper, the authors demonstrate that neural networks and quantum computers can be executed with the same physical platform, based on photonics, which provides a natural extension of classical machine learning algorithms into the quantum realm.
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