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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Journal ArticleDOI
Image classification using collective optical modes of an array of nanolasers
TL;DR: In this article , the authors exploit the symmetry properties of the collective modes of a nanolaser array for a simple binary classification task of small digits images, which relies on the activation of a collective optical mode of the array-the so-called zero mode'-, under spatially modulated pump patterns.
Journal ArticleDOI
Intelligent optoelectronic processor for orbital angular momentum spectrum measurement
TL;DR: In this paper , an intelligent processor composed of photonic and electronic neurons for OAM spectrum measurement was proposed, where optical layers extract invisible topological charge information from incoming light and a shallow electronic layer predicts the exact spectrum.
Posted Content
Optical Proof of Work.
TL;DR: The authors propose a novel PoW algorithm, Optical Proof of Work (oPoW), to eliminate energy as the primary cost of mining, and has the potential to improve network scalability, enable decentralized mining outside of low electricity cost areas, and democratize issuance.
Patent
Optoelectronic computing systems
Yichen Shen,Li Jing,Rumen Dangovski,Peng Xie,Huaiyu Meng,Matthew Khoury,Lu Cheng-Kuan,Gagnon Ronald,Steinman Maurice,Jianhua Wu,Hosseinzadeh Arash +10 more
TL;DR: In this article, the optical modulators in the first set are configured to generate an optical input vector by modulating the plurality of light outputs provided by the light source or port based on digital input values corresponding to a first set of modulator control signals.
Journal ArticleDOI
Bayesian Photonic Accelerators for Energy Efficient and Noise Robust Neural Processing
TL;DR: Two novel training schemes are derived, namely a regularized version and a fully Bayesian learning scheme applied on a photonic neural network with 512 phase shifters targeting the MNIST dataset, able to dramatically decrease the operational power of the PIC beyond 70%, with just a slight loss in classification accuracy.
References
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