Prospects and applications of photonic neural networks

被引：127

作者：

Huang, Chaoran ^{[1
,2
]}

Sorger, Volker J. ^{[3
]}

Miscuglio, Mario ^{[3
]}

Al-Qadasi, Mohammed

Mukherjee, Avilash ^{[4
]}

Lampe, Lutz ^{[4
]}

Nichols, Mitchell ^{[4
]}

Tait, Alexander N. ^{[5
]}

Ferreira de Lima, Thomas ^{[1
]}

Marquez, Bicky A. ^{[6
]}

Wang, Jiahui ^{[7
]}

Chrostowski, Lukas ^{[4
]}

Fok, Mable P. ^{[8
]}

Brunner, Daniel ^{[9
]}

Fan, Shanhui ^{[7
]}

Shekhar, Sudip ^{[4
]}

Prucnal, Paul R. ^{[1
]}

Shastri, Bhavin J. ^{[6
,10
]}

机构：

[1] Princeton Univ, Dept Elect & Comp Engn, Princeton, NJ 08544 USA

[2] Chinese Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China

[3] George Washington Univ, Dept Elect & Comp Engn, Washington, DC USA

[4] Univ British Columbia, Dept Elect & Comp Engn, Vancouver, BC, Canada

[5] NIST, Boulder, CO USA

[6] Queens Univ, Dept Elect Engn, Kingston, ON, Canada

[7] Stanford Univ, Coll Engn, Stanford, CA 94305 USA

[8] Univ Georgia, Athens, GA USA

[9] Univ Bourgogne Franche Comte, Inst FEMTO ST, Dept Phys Engn Phys & Astron, CNRS,UMR, Besancon, France

[10] Vector Inst, Toronto, ON, Canada

来源：

ADVANCES IN PHYSICS-X | 2022年 / 7卷 / 01期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Photonic neural networks; neuromorphic photonics; silicon photonics; neuromorphic computing; machine learning; artificial intelligence; MACH-ZEHNDER MODULATOR; OPTICAL FLIP-FLOP; SAR ADC; ARTIFICIAL-INTELLIGENCE; ULTRA-COMPACT; DESIGN; ENERGY; ANALOG; BACKPROPAGATION; IMPLEMENTATION;

D O I：

10.1080/23746149.2021.1981155

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Neural networks have enabled applications in artificial intelligence through machine learning, and neuromorphic computing. Software implementations of neural networks on conventional computers that have separate memory and processor (and that operate sequentially) are limited in speed and energy efficiency. Neuromorphic engineering aims to build processors in which hardware mimics neurons and synapses in the brain for distributed and parallel processing. Neuromorphic engineering enabled by photonics (optical physics) can offer sub-nanosecond latencies and high bandwidth with low energies to extend the domain of artificial intelligence and neuromorphic computing applications to machine learning acceleration, nonlinear programming, intelligent signal processing, etc. Photonic neural networks have been demonstrated on integrated platforms and free-space optics depending on the class of applications being targeted. Here, we discuss the prospects and demonstrated applications of these photonic neural networks.

引用

页数：63

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