Energy Efficient Superconducting Neural Networks for High-Speed Intellectual Data Processing Systems
Publication type: Journal Article
Publication date: 2018-10-01
scimago Q2
wos Q3
SJR: 0.508
CiteScore: 3.4
Impact factor: 1.8
ISSN: 10518223, 15582515, 23787074
Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Electrical and Electronic Engineering
Abstract
We present the results of circuit simulations for the adiabatic flux-operating neuron. The proposed cell with one-shot calculation of activation function is based on a modified single-junction superconducting quantum interferometer. In comparison, functionally equivalent elements of the artificial neural network (ANN) in the semiconductor-based implementations consist of approximately 20 transistors. Also in the article, we present the connecting synapse based on the adiabatic quantum flux parametron. These neurons and synapses allow constructing ANNs with a magnetic representation of information in the form of direction and/or magnitude of the magnetic flux in the superconducting circuit. We discuss the dissipation of energy during operations in the frame of the proposed concept. This value in superconducting neurons and synapses with sub-nanosecond timescale can be reduced down to 10 and 0.1 aJ, respectively. The use of the adiabatic superconducting logic circuits in our approach promises compatibility with superconducting quantum information processing systems.
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Total citations:
22
Citations from 2025:
2
(9.52%)
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GOST
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Klenov N. V. et al. Energy Efficient Superconducting Neural Networks for High-Speed Intellectual Data Processing Systems // IEEE Transactions on Applied Superconductivity. 2018. Vol. 28. No. 7. pp. 1-6.
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Klenov N. V., Schegolev A. E., Soloviev I. I., Bakurskiy S. V., Tereshonok M. V. Energy Efficient Superconducting Neural Networks for High-Speed Intellectual Data Processing Systems // IEEE Transactions on Applied Superconductivity. 2018. Vol. 28. No. 7. pp. 1-6.
Cite this
RIS
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TY - JOUR
DO - 10.1109/TASC.2018.2836903
UR - https://doi.org/10.1109/TASC.2018.2836903
TI - Energy Efficient Superconducting Neural Networks for High-Speed Intellectual Data Processing Systems
T2 - IEEE Transactions on Applied Superconductivity
AU - Klenov, Nikolay V
AU - Schegolev, Andrey E
AU - Soloviev, Igor I.
AU - Bakurskiy, Sergey V
AU - Tereshonok, Maxim V
PY - 2018
DA - 2018/10/01
PB - Institute of Electrical and Electronics Engineers (IEEE)
SP - 1-6
IS - 7
VL - 28
SN - 1051-8223
SN - 1558-2515
SN - 2378-7074
ER -
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BibTex (up to 50 authors)
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@article{2018_Klenov,
author = {Nikolay V Klenov and Andrey E Schegolev and Igor I. Soloviev and Sergey V Bakurskiy and Maxim V Tereshonok},
title = {Energy Efficient Superconducting Neural Networks for High-Speed Intellectual Data Processing Systems},
journal = {IEEE Transactions on Applied Superconductivity},
year = {2018},
volume = {28},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
month = {oct},
url = {https://doi.org/10.1109/TASC.2018.2836903},
number = {7},
pages = {1--6},
doi = {10.1109/TASC.2018.2836903}
}
Cite this
MLA
Copy
Klenov, Nikolay V., et al. “Energy Efficient Superconducting Neural Networks for High-Speed Intellectual Data Processing Systems.” IEEE Transactions on Applied Superconductivity, vol. 28, no. 7, Oct. 2018, pp. 1-6. https://doi.org/10.1109/TASC.2018.2836903.