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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All-optical synthesis of an arbitrary linear transformation using diffractive surfaces
TL;DR: In this article, the authors report the design of diffractive surfaces to all-optically perform arbitrary complex-valued linear transformations between an input (Ni) and output (No), where Ni and No represent the number of pixels at the input and output fields-of-view (FOVs), respectively.
Posted Content
A Survey on Silicon Photonics for Deep Learning
TL;DR: The landscape of silicon photonics to accelerate deep learning is surveyed, with a coverage of developments across design abstractions in a bottom-up manner, to convey both the capabilities and limitations of the silicon Photonics paradigm in the context of deep learning acceleration.
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Neuromorphic Silicon Photonics and Hardware-Aware Deep Learning for High-Speed Inference
Miltiadis Moralis-Pegios,George Mourgias-Alexandris,Apostolos Tsakyridis,George Giamougiannis,Angelina Totovic,George Dabos,Nikolaos Passalis,Manos Kirtas,Teerapat Rutirawut,F. Gardes,Anastasios Tefas,Nikos Pleros +11 more
TL;DR: This paper reviews recent progress in integrated photonic neuromorphic architectures and analyzes the architectural and photonic hardware-based factors that limit their performance, and presents the approach towards transforming silicon coherent neuromorphic layouts into high-speed and high-accuracy Deep Learning (DL) engines by combining robust architectures with hardware-aware DL training.
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Classification of time-domain waveforms using a speckle-based optical reservoir computer.
TL;DR: A bulk electro-optical demonstration of a reservoir computer using speckles generated by propagating a laser beam modulated with a spatial light modulator through a multimode waveguide to demonstrate a framework for building a scalable, chip-scale, reservoir computer capable of performing optical signal processing.
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Self-learning photonic signal processor with an optical neural network chip
TL;DR: The proposed photonic signal processor is capable of performing various functions including multichannel optical switching, optical multiple-input-multiple-output descrambler and tunable optical filter and all the functions are achieved by complete self-learning.
References
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