The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Avec les progrès rapides de la technologie nanophotonique intégrée, l’architecture du réseau neuronal optique a été largement étudiée. Étant donné que le réseau neuronal optique peut terminer le traitement d'inférence simplement en propageant le signal optique dans le réseau, il devrait être plus d'un ordre de grandeur plus rapide que la mise en œuvre uniquement électronique de réseaux neuronaux artificiels (ANN). Dans cet article, nous proposons d'abord un circuit de multiplication optique à matrice vectorielle (VMM) utilisant le multiplexage par répartition en longueur d'onde, qui permet un traitement d'inférence à la vitesse de la lumière avec une bande ultra-large. Cet article propose ensuite la mise en œuvre d'un circuit optoélectronique pour la fonction de normalisation et d'activation par lots, ce qui améliore considérablement la précision du traitement d'inférence sans sacrifier les performances en termes de vitesse. Enfin, à l’aide d’un environnement virtuel d’apprentissage automatique et d’un simulateur de circuit optoélectronique, nous démontrons le fonctionnement ultra-rapide et précis du circuit ANN optique-électronique.
Naoki HATTORI
Nagoya University
Jun SHIOMI
Kyoto University
Yutaka MASUDA
Nagoya University
Tohru ISHIHARA
Nagoya University
Akihiko SHINYA
NTT Nanophotonics Center,NTT Basic Research Laboratories
Masaya NOTOMI
NTT Nanophotonics Center,NTT Basic Research Laboratories
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Naoki HATTORI, Jun SHIOMI, Yutaka MASUDA, Tohru ISHIHARA, Akihiko SHINYA, Masaya NOTOMI, "Neural Network Calculations at the Speed of Light Using Optical Vector-Matrix Multiplication and Optoelectronic Activation" in IEICE TRANSACTIONS on Fundamentals,
vol. E104-A, no. 11, pp. 1477-1487, November 2021, doi: 10.1587/transfun.2020KEP0016.
Abstract: With the rapid progress of the integrated nanophotonics technology, the optical neural network architecture has been widely investigated. Since the optical neural network can complete the inference processing just by propagating the optical signal in the network, it is expected more than one order of magnitude faster than the electronics-only implementation of artificial neural networks (ANN). In this paper, we first propose an optical vector-matrix multiplication (VMM) circuit using wavelength division multiplexing, which enables inference processing at the speed of light with ultra-wideband. This paper next proposes optoelectronic circuit implementation for batch normalization and activation function, which significantly improves the accuracy of the inference processing without sacrificing the speed performance. Finally, using a virtual environment for machine learning and an optoelectronic circuit simulator, we demonstrate the ultra-fast and accurate operation of the optical-electronic ANN circuit.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020KEP0016/_p
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@ARTICLE{e104-a_11_1477,
author={Naoki HATTORI, Jun SHIOMI, Yutaka MASUDA, Tohru ISHIHARA, Akihiko SHINYA, Masaya NOTOMI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Neural Network Calculations at the Speed of Light Using Optical Vector-Matrix Multiplication and Optoelectronic Activation},
year={2021},
volume={E104-A},
number={11},
pages={1477-1487},
abstract={With the rapid progress of the integrated nanophotonics technology, the optical neural network architecture has been widely investigated. Since the optical neural network can complete the inference processing just by propagating the optical signal in the network, it is expected more than one order of magnitude faster than the electronics-only implementation of artificial neural networks (ANN). In this paper, we first propose an optical vector-matrix multiplication (VMM) circuit using wavelength division multiplexing, which enables inference processing at the speed of light with ultra-wideband. This paper next proposes optoelectronic circuit implementation for batch normalization and activation function, which significantly improves the accuracy of the inference processing without sacrificing the speed performance. Finally, using a virtual environment for machine learning and an optoelectronic circuit simulator, we demonstrate the ultra-fast and accurate operation of the optical-electronic ANN circuit.},
keywords={},
doi={10.1587/transfun.2020KEP0016},
ISSN={1745-1337},
month={November},}
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TY - JOUR
TI - Neural Network Calculations at the Speed of Light Using Optical Vector-Matrix Multiplication and Optoelectronic Activation
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1477
EP - 1487
AU - Naoki HATTORI
AU - Jun SHIOMI
AU - Yutaka MASUDA
AU - Tohru ISHIHARA
AU - Akihiko SHINYA
AU - Masaya NOTOMI
PY - 2021
DO - 10.1587/transfun.2020KEP0016
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E104-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2021
AB - With the rapid progress of the integrated nanophotonics technology, the optical neural network architecture has been widely investigated. Since the optical neural network can complete the inference processing just by propagating the optical signal in the network, it is expected more than one order of magnitude faster than the electronics-only implementation of artificial neural networks (ANN). In this paper, we first propose an optical vector-matrix multiplication (VMM) circuit using wavelength division multiplexing, which enables inference processing at the speed of light with ultra-wideband. This paper next proposes optoelectronic circuit implementation for batch normalization and activation function, which significantly improves the accuracy of the inference processing without sacrificing the speed performance. Finally, using a virtual environment for machine learning and an optoelectronic circuit simulator, we demonstrate the ultra-fast and accurate operation of the optical-electronic ANN circuit.
ER -