Journal of Applied Physics, volume 124, issue 15, pages 152113
Adiabatic superconducting artificial neural network: Basic cells
Soloviev Igor I
1, 2, 3
,
Schegolev Andrey E.
2, 3, 4, 5
,
Klenov Nikolay V.
2, 3, 4, 5, 6
,
Bakurskiy Sergey V
1, 2, 3
,
Tereshonok M.V.
2, 4
,
Shadrin Anton V
1
,
Stolyarov V. S.
1, 6, 7, 8
,
Golubov Alexander A
1, 9
3
7
Solid State Physics Department, KFU 8 , 420008 Kazan, Russia
|
9
Faculty of Science and Technology and MESA+ Institute of Nanotechnology 9 , 7500 AE Enschede, The Netherlands
|
Publication type: Journal Article
Publication date: 2018-09-26
Journal:
Journal of Applied Physics
Quartile SCImago
Q2
Quartile WOS
Q2
Impact factor: 3.2
ISSN: 00218979, 10897550
General Physics and Astronomy
Abstract
We consider adiabatic superconducting cells operating as an artificial neuron and synapse of a multilayer perceptron (MLP). Their compact circuits contain just one and two Josephson junctions, respectively. While the signal is represented as magnetic flux, the proposed cells are inherently nonlinear and close-to-linear magnetic flux transformers. The neuron is capable of providing the one-shot calculation of sigmoid and hyperbolic tangent activation functions most commonly used in MLP. The synapse features both positive and negative signal transfer coefficients in the range ∼ ( − 0.5 , 0.5 ). We briefly discuss implementation issues and further steps toward the multilayer adiabatic superconducting artificial neural network, which promises to be a compact and the most energy-efficient implementation of MLP.We consider adiabatic superconducting cells operating as an artificial neuron and synapse of a multilayer perceptron (MLP). Their compact circuits contain just one and two Josephson junctions, respectively. While the signal is represented as magnetic flux, the proposed cells are inherently nonlinear and close-to-linear magnetic flux transformers. The neuron is capable of providing the one-shot calculation of sigmoid and hyperbolic tangent activation functions most commonly used in MLP. The synapse features both positive and negative signal transfer coefficients in the range ∼ ( − 0.5 , 0.5 ). We briefly discuss implementation issues and further steps toward the multilayer adiabatic superconducting artificial neural network, which promises to be a compact and the most energy-efficient implementation of MLP.
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- We do not take into account publications that without a DOI.
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- Statistics recalculated weekly.
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Soloviev I. I. et al. Adiabatic superconducting artificial neural network: Basic cells // Journal of Applied Physics. 2018. Vol. 124. No. 15. p. 152113.
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Soloviev I. I., Schegolev A. E., Klenov N. V., Bakurskiy S. V., Kupriyanov M. Yu., Tereshonok M., Shadrin A. V., Stolyarov V. S., Golubov A. A. Adiabatic superconducting artificial neural network: Basic cells // Journal of Applied Physics. 2018. Vol. 124. No. 15. p. 152113.
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TY - JOUR
DO - 10.1063/1.5042147
UR - https://doi.org/10.1063%2F1.5042147
TI - Adiabatic superconducting artificial neural network: Basic cells
T2 - Journal of Applied Physics
AU - Soloviev, Igor I
AU - Schegolev, Andrey E.
AU - Klenov, Nikolay V.
AU - Bakurskiy, Sergey V
AU - Kupriyanov, Mikhail Yu
AU - Tereshonok, M.V.
AU - Shadrin, Anton V
AU - Stolyarov, V. S.
AU - Golubov, Alexander A
PY - 2018
DA - 2018/09/26 00:00:00
PB - American Institute of Physics (AIP)
SP - 152113
IS - 15
VL - 124
SN - 0021-8979
SN - 1089-7550
ER -
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@article{2018_Soloviev,
author = {Igor I Soloviev and Andrey E. Schegolev and Nikolay V. Klenov and Sergey V Bakurskiy and Mikhail Yu Kupriyanov and M.V. Tereshonok and Anton V Shadrin and V. S. Stolyarov and Alexander A Golubov},
title = {Adiabatic superconducting artificial neural network: Basic cells},
journal = {Journal of Applied Physics},
year = {2018},
volume = {124},
publisher = {American Institute of Physics (AIP)},
month = {sep},
url = {https://doi.org/10.1063%2F1.5042147},
number = {15},
pages = {152113},
doi = {10.1063/1.5042147}
}
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MLA
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Soloviev, Igor I., et al. “Adiabatic superconducting artificial neural network: Basic cells.” Journal of Applied Physics, vol. 124, no. 15, Sep. 2018, p. 152113. https://doi.org/10.1063%2F1.5042147.