Physical modeling of photonic neural networks

Thomas Ferreira De Lima; Bhavin J. Shastri; Mitchell A. Nahmias; Alexander N. Tait; Paul R. Prucnal

doi:10.1109/PHOSST.2016.7548808

Physical modeling of photonic neural networks

Thomas Ferreira De Lima, Bhavin J. Shastri, Mitchell A. Nahmias, Alexander N. Tait, Paul R. Prucnal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

Brain-inspired distributed computing has attracted attention for its energy-efficient processing, and photonic neuromorphic hardware can overcome latency vs. fan-in tradeoffs from neuromorphic electronics. Here, we introduce system-level, physically detailed modeling tools for photonic neural networks, and use them to study the behavior of attractor networks.

Original language	English (US)
Title of host publication	2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	222-223
Number of pages	2
ISBN (Electronic)	9781509019007
DOIs	https://doi.org/10.1109/PHOSST.2016.7548808
State	Published - Aug 22 2016
Event	2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016 - Newport Beach, United States Duration: Jul 11 2016 → Jul 13 2016

Publication series

Name	2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016

Other

Other	2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016
Country/Territory	United States
City	Newport Beach
Period	7/11/16 → 7/13/16

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Signal Processing
Atomic and Molecular Physics, and Optics
Electrical and Electronic Engineering

Access to Document

10.1109/PHOSST.2016.7548808

Cite this

De Lima, T. F., Shastri, B. J., Nahmias, M. A., Tait, A. N., & Prucnal, P. R. (2016). Physical modeling of photonic neural networks. In 2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016 (pp. 222-223). Article 7548808 (2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PHOSST.2016.7548808

@inproceedings{4be49e2c9044419b9a5485675cc420e2,

title = "Physical modeling of photonic neural networks",

abstract = "Brain-inspired distributed computing has attracted attention for its energy-efficient processing, and photonic neuromorphic hardware can overcome latency vs. fan-in tradeoffs from neuromorphic electronics. Here, we introduce system-level, physically detailed modeling tools for photonic neural networks, and use them to study the behavior of attractor networks.",

author = "{De Lima}, {Thomas Ferreira} and Shastri, {Bhavin J.} and Nahmias, {Mitchell A.} and Tait, {Alexander N.} and Prucnal, {Paul R.}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016 ; Conference date: 11-07-2016 Through 13-07-2016",

year = "2016",

month = aug,

day = "22",

doi = "10.1109/PHOSST.2016.7548808",

language = "English (US)",

series = "2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "222--223",

booktitle = "2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016",

address = "United States",

}

De Lima, TF, Shastri, BJ, Nahmias, MA, Tait, AN & Prucnal, PR 2016, Physical modeling of photonic neural networks. in 2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016., 7548808, 2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016, Institute of Electrical and Electronics Engineers Inc., pp. 222-223, 2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016, Newport Beach, United States, 7/11/16. https://doi.org/10.1109/PHOSST.2016.7548808

Physical modeling of photonic neural networks. / De Lima, Thomas Ferreira; Shastri, Bhavin J.; Nahmias, Mitchell A. et al.
2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 222-223 7548808 (2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Physical modeling of photonic neural networks

AU - De Lima, Thomas Ferreira

AU - Shastri, Bhavin J.

AU - Nahmias, Mitchell A.

AU - Tait, Alexander N.

AU - Prucnal, Paul R.

PY - 2016/8/22

Y1 - 2016/8/22

N2 - Brain-inspired distributed computing has attracted attention for its energy-efficient processing, and photonic neuromorphic hardware can overcome latency vs. fan-in tradeoffs from neuromorphic electronics. Here, we introduce system-level, physically detailed modeling tools for photonic neural networks, and use them to study the behavior of attractor networks.

AB - Brain-inspired distributed computing has attracted attention for its energy-efficient processing, and photonic neuromorphic hardware can overcome latency vs. fan-in tradeoffs from neuromorphic electronics. Here, we introduce system-level, physically detailed modeling tools for photonic neural networks, and use them to study the behavior of attractor networks.

UR - http://www.scopus.com/inward/record.url?scp=84988702973&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84988702973&partnerID=8YFLogxK

U2 - 10.1109/PHOSST.2016.7548808

DO - 10.1109/PHOSST.2016.7548808

M3 - Conference contribution

AN - SCOPUS:84988702973

T3 - 2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016

SP - 222

EP - 223

BT - 2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE Photonics Society Summer Topical Meeting Series, SUM 2016

Y2 - 11 July 2016 through 13 July 2016

ER -

Physical modeling of photonic neural networks

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this