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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Low-crosstalk, Low-power Mach-Zehnder Interferometer Optical Switch Based on III-V/Si Hybrid MOS Phase Shifter
TL;DR: An optical switch with In GaAsP/Si hybrid metal-oxide-semiconductor phase shifter is demonstrated with low crosstalk and low power consumption due to the large electron-induced refractive index change and small absorption in InGaAsP.
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Image classification using delay-based optoelectronic reservoir computing
TL;DR: A new optoelectronic reservoir computer for image recognition is introduced, in which input data is first pre-processed offline using two convolutional neural network layers with randomly initialized weights, generating a series of random feature maps.
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Hardware Architecture and Algorithm Co-Design for Multi-Layer Photonic Neuromorphic Network with Excitable VCSELs-SA
TL;DR: Numerical results based on the rate-equation models show that the proposed neuromorphic network architecture is capable of solving the classical XOR problem by supervised-learning.
Posted Content
Breaking Reciprocity in Integrated Photonic Devices Through Dynamic Modulation
TL;DR: In this article, the authors review theoretical and experimental progress towards developing non-reciprocal photonic devices based on dynamic modulation, focusing on approaches that operate at roughlyoptical wavelengths and device architectures that have the potential for chip-scale integration.
Proceedings ArticleDOI
Error-Tolerant Integrated Optical Neural Network Processor based on Multi-Plane Light Conversion
TL;DR: In this paper , the authors demonstrate integrated optical neural network processor with excellent error tolerance using multiport directional couplers, thanks to robust multi-plane light-conversion mechanism, high data-classifying accuracy over 95% is confirmed, insensitive to the exact coupling ratio.
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