Matrix Multiplication Unit Scalability Investigation for InP SOA-based Photonic Deep Neural Network

B. Shi; N. Calabretta; R. Stabile

doi:10.1109/SUM48678.2020.9161056

Matrix Multiplication Unit Scalability Investigation for InP SOA-based Photonic Deep Neural Network

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

We investigate the scalability of a SOA-based photonic DNN with WDM inputs. For a 3-layer DNN, a quasi-linear dependence between the prediction accuracy and errors/layer is found for a NRMSE <0.09. Optimized passive losses can enable synaptic function at half of the power consumption.

Original language	English
Title of host publication	2020 IEEE Photonics Society Summer Topical Meeting Series, SUM 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers
Number of pages	2
ISBN (Electronic)	978-1-7281-5887-7
DOIs	https://doi.org/10.1109/SUM48678.2020.9161056
Publication status	Published - Jul 2020
Event	2020 IEEE Photonics Society Summer Topical Meeting Series, SUM 2020 - Cabo San Lucas, Mexico Duration: 13 Jul 2020 → 15 Jul 2020

Conference

Conference	2020 IEEE Photonics Society Summer Topical Meeting Series, SUM 2020
Country/Territory	Mexico
City	Cabo San Lucas
Period	13/07/20 → 15/07/20

Keywords

Artificial neural networks
Image classification
Photonic integrated circuits
Semiconductor optical amplifiers
Simulation

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10.1109/SUM48678.2020.9161056

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@inproceedings{b8421244daac47359db13a3f33721ada,

title = "Matrix Multiplication Unit Scalability Investigation for InP SOA-based Photonic Deep Neural Network",

abstract = "We investigate the scalability of a SOA-based photonic DNN with WDM inputs. For a 3-layer DNN, a quasi-linear dependence between the prediction accuracy and errors/layer is found for a NRMSE <0.09. Optimized passive losses can enable synaptic function at half of the power consumption.",

keywords = "Artificial neural networks, Image classification, Photonic integrated circuits, Semiconductor optical amplifiers, Simulation",

author = "B. Shi and N. Calabretta and R. Stabile",

year = "2020",

month = jul,

doi = "10.1109/SUM48678.2020.9161056",

language = "English",

booktitle = "2020 IEEE Photonics Society Summer Topical Meeting Series, SUM 2020 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2020 IEEE Photonics Society Summer Topical Meeting Series, SUM 2020 ; Conference date: 13-07-2020 Through 15-07-2020",

}

Shi, B , Calabretta, N & Stabile, R 2020, Matrix Multiplication Unit Scalability Investigation for InP SOA-based Photonic Deep Neural Network. in 2020 IEEE Photonics Society Summer Topical Meeting Series, SUM 2020 - Proceedings., 9161056, Institute of Electrical and Electronics Engineers, 2020 IEEE Photonics Society Summer Topical Meeting Series, SUM 2020, Cabo San Lucas, Mexico, 13/07/20. https://doi.org/10.1109/SUM48678.2020.9161056

Matrix Multiplication Unit Scalability Investigation for InP SOA-based Photonic Deep Neural Network. / Shi, B.; Calabretta, N.; Stabile, R.

2020 IEEE Photonics Society Summer Topical Meeting Series, SUM 2020 - Proceedings. Institute of Electrical and Electronics Engineers, 2020. 9161056.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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N2 - We investigate the scalability of a SOA-based photonic DNN with WDM inputs. For a 3-layer DNN, a quasi-linear dependence between the prediction accuracy and errors/layer is found for a NRMSE <0.09. Optimized passive losses can enable synaptic function at half of the power consumption.

AB - We investigate the scalability of a SOA-based photonic DNN with WDM inputs. For a 3-layer DNN, a quasi-linear dependence between the prediction accuracy and errors/layer is found for a NRMSE <0.09. Optimized passive losses can enable synaptic function at half of the power consumption.

KW - Artificial neural networks

KW - Image classification

KW - Photonic integrated circuits

KW - Semiconductor optical amplifiers

KW - Simulation

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M3 - Conference contribution

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BT - 2020 IEEE Photonics Society Summer Topical Meeting Series, SUM 2020 - Proceedings

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Y2 - 13 July 2020 through 15 July 2020

ER -

Matrix Multiplication Unit Scalability Investigation for InP SOA-based Photonic Deep Neural Network

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