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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Noise mitigation strategies in physical feedforward neural networks
Nadezhda Semenova,Daniel Brunner +1 more
TL;DR: This work analytically shows that intra-layer connections in which the connection matrix's squared mean exceeds the mean of its square fully suppress uncorrelated noise, and developed a general noise-mitigation strategy leveraging the statistical properties of the different noise terms most relevant in analog hardware.
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Investigation on Expanding the Computing Capacity of Optical Programmable Logic Array Based on Canonical Logic Units
TL;DR: A general structure of an all-optical canonical logic based programmable logic array (CLUs-PLA) based on three methods to expand its computing capacity is presented, including introducing bidirectional structure for nonlinear devices, utilizing wavelength multicast of four-wave mixing (FWM), and exploiting different nonlinear effects simultaneously.
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Co-Design of Free-Space Metasurface Optical Neuromorphic Classifiers for High Performance
TL;DR: It is shown that system architecture, material structure, and input light field are intertwined and need to be co-designed to maximize classification accuracy, and that once the system is optimized, the number of diffractive features is the main determinant of classification performance.
Proceedings ArticleDOI
Demonstration of Classification Task Using Optical Neural Network Based on Si Microring Resonator Crossbar Array
TL;DR: In this article, Si microring resonator crossbar array was used as a programmable nanophotonic processor for optical neural network for classification task for Iris dataset, resulting in prediction accuracy of 91%.
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Robust Architecture-Agnostic and Noise Resilient Training of Photonic Deep Learning Models
TL;DR: In this article , the authors propose a novel training method for photonic neuromorphic architectures that is capable of taking into account a wide range of limitations of the actual hardware, including noise sources and easily saturated activation mechanisms.
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