volume 28 issue 7 pages 1-6

Energy Efficient Superconducting Neural Networks for High-Speed Intellectual Data Processing Systems

Publication typeJournal Article
Publication date2018-10-01
scimago Q2
wos Q3
SJR0.508
CiteScore3.4
Impact factor1.8
ISSN10518223, 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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GOST Copy
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.
GOST all authors (up to 50) Copy
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.
RIS |
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RIS Copy
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 -
BibTex |
Cite this
BibTex (up to 50 authors) Copy
@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}
}
MLA
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.