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Open AccessJournal ArticleDOI

Deep learning with coherent nanophotonic circuits

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.

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Citations
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Journal ArticleDOI

Machine learning and the physical sciences

TL;DR: This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences, including conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields.
Journal ArticleDOI

Integrated lithium niobate electro-optic modulators operating at CMOS-compatible voltages

TL;DR: Monolithically integrated lithium niobate electro-optic modulators that feature a CMOS-compatible drive voltage, support data rates up to 210 gigabits per second and show an on-chip optical loss of less than 0.5 decibels are demonstrated.
Journal ArticleDOI

All-optical machine learning using diffractive deep neural networks

TL;DR: 3D-printed D2NNs are created that implement classification of images of handwritten digits and fashion products, as well as the function of an imaging lens at a terahertz spectrum.
Journal ArticleDOI

All-optical spiking neurosynaptic networks with self-learning capabilities.

TL;DR: An optical version of a brain-inspired neurosynaptic system, using wavelength division multiplexing techniques, is presented that is capable of supervised and unsupervised learning.
Journal ArticleDOI

Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures

TL;DR: A tandem neural network architecture is demonstrated that tolerates inconsistent training instances in inverse design of nanophotonic devices and provides a way to train large neural networks for the inverseDesign of complex photonic structures.
References
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Posted Content

A 25 Gb/s Silicon Photonics Platform

TL;DR: In this article, an optical-lithography, wafer-scale photonics platform with 25 Gb/s operation was demonstrated, with an ultra-low drive voltage of 1 Vpp at 25 GHz.
Book ChapterDOI

Photonic Neuromorphic Signal Processing and Computing

TL;DR: In this paper, a technique for mapping the spike encoding paradigm to scalable, integrated laser devices is explored and simulated in small networks, which could allow for fully scalable photonic networks that would open up a new domain of ultrafast, robust, and adaptive processing.
Journal ArticleDOI

Experimental observations of bistability and instability in a two-dimensional nonlinear optical superlattice.

TL;DR: Optical bistability in a two-dimensional nonlinear superlattice, resulting from the index-modulation mechanism, was observed in experiment for the first time.
Journal ArticleDOI

Nonlinear mirror based on two-photon absorption

TL;DR: In this paper, the properties of a nonlinear mirror design based on two-photon absorption were investigated and it was shown that the reflectivity of this structure increases with intensity over certain wavelength ranges.
Book ChapterDOI

Speaker identification based on log area ratio and Gaussian mixture models in narrow-band speech: speech understanding/interaction

TL;DR: An F-ratio feature analysis was conducted on both the LAR and MFCC feature vectors which showed the lower order LAR coefficients are superior to MFCC counterpart, and the text- independent, closed-set speaker identification rate was improved.
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