Inference and Gradient Measurement for Backpropagation in Photonic Neural Networks

Sunil Pai; Tyler W. Hughes; Taewon Park; Ben Bartlett; Ian Williamson; Momchil Minkov; Maziyar Milanizadeh; Nathnael Abebe; Francesco Morichetti; Andrea Melloni; Olav Solgaard; Shanhui Fan; David A.B. Miller

Inference and Gradient Measurement for Backpropagation in Photonic Neural Networks

Sunil Pai
, Tyler W. Hughes
, Taewon Park
, Ben Bartlett
, Ian Williamson
, Momchil Minkov
, Maziyar Milanizadeh
, Nathnael Abebe
, Francesco Morichetti
, Andrea Melloni
, Olav Solgaard
, Shanhui Fan
, David A.B. Miller

Research output: Contribution to journal › Conference article › peer-review

Abstract

We experimentally demonstrate in situ backpropagation in a programmable nanophotonic interferometer network, achieving inference accuracies matching digital implementations. Error gradients are computed by simultaneously measuring optical interference at intermediate network components, eliminating expensive digital computations.

Original language	English
Article number	STh5G.2
Journal	Optics InfoBase Conference Papers
Publication status	Published - 2022
Externally published	Yes
Event	CLEO: Science and Innovations, S and I 2022 - San Jose, United States Duration: 15 May 2022 → 20 May 2022

Cite this

@article{7fb4d52dc83542318509fe0da4e8a458,

title = "Inference and Gradient Measurement for Backpropagation in Photonic Neural Networks",

abstract = "We experimentally demonstrate in situ backpropagation in a programmable nanophotonic interferometer network, achieving inference accuracies matching digital implementations. Error gradients are computed by simultaneously measuring optical interference at intermediate network components, eliminating expensive digital computations.",

author = "Sunil Pai and Hughes, \{Tyler W.\} and Taewon Park and Ben Bartlett and Ian Williamson and Momchil Minkov and Maziyar Milanizadeh and Nathnael Abebe and Francesco Morichetti and Andrea Melloni and Olav Solgaard and Shanhui Fan and Miller, \{David A.B.\}",

note = "Publisher Copyright: {\textcopyright} Optica Publishing Group 2022, {\textcopyright} 2022 The Author(s); CLEO: Science and Innovations, S and I 2022 ; Conference date: 15-05-2022 Through 20-05-2022",

year = "2022",

language = "English",

journal = "Optics InfoBase Conference Papers",

issn = "2162-2701",

}

TY - JOUR

T1 - Inference and Gradient Measurement for Backpropagation in Photonic Neural Networks

AU - Pai, Sunil

AU - Hughes, Tyler W.

AU - Park, Taewon

AU - Bartlett, Ben

AU - Williamson, Ian

AU - Minkov, Momchil

AU - Milanizadeh, Maziyar

AU - Abebe, Nathnael

AU - Morichetti, Francesco

AU - Melloni, Andrea

AU - Solgaard, Olav

AU - Fan, Shanhui

AU - Miller, David A.B.

PY - 2022

Y1 - 2022

N2 - We experimentally demonstrate in situ backpropagation in a programmable nanophotonic interferometer network, achieving inference accuracies matching digital implementations. Error gradients are computed by simultaneously measuring optical interference at intermediate network components, eliminating expensive digital computations.

AB - We experimentally demonstrate in situ backpropagation in a programmable nanophotonic interferometer network, achieving inference accuracies matching digital implementations. Error gradients are computed by simultaneously measuring optical interference at intermediate network components, eliminating expensive digital computations.

UR - https://www.scopus.com/pages/publications/85136796760

M3 - Conference article

AN - SCOPUS:85136796760

SN - 2162-2701

JO - Optics InfoBase Conference Papers

JF - Optics InfoBase Conference Papers

M1 - STh5G.2

T2 - CLEO: Science and Innovations, S and I 2022

Y2 - 15 May 2022 through 20 May 2022

ER -

Inference and Gradient Measurement for Backpropagation in Photonic Neural Networks

Abstract

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