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dc.contributor.author | Soloviev I. | |
dc.contributor.author | Schegolev A. | |
dc.contributor.author | Klenov N. | |
dc.contributor.author | Bakurskiy S. | |
dc.contributor.author | Kupriyanov M. | |
dc.contributor.author | Tereshonok M. | |
dc.contributor.author | Shadrin A. | |
dc.contributor.author | Stolyarov V. | |
dc.contributor.author | Golubov A. | |
dc.date.accessioned | 2019-01-22T20:32:49Z | |
dc.date.available | 2019-01-22T20:32:49Z | |
dc.date.issued | 2018 | |
dc.identifier.issn | 0021-8979 | |
dc.identifier.uri | https://dspace.kpfu.ru/xmlui/handle/net/147651 | |
dc.description.abstract | © 2018 Author(s). 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. | |
dc.relation.ispartofseries | Journal of Applied Physics | |
dc.title | Adiabatic superconducting artificial neural network: Basic cells | |
dc.type | Article | |
dc.relation.ispartofseries-issue | 15 | |
dc.relation.ispartofseries-volume | 124 | |
dc.collection | Публикации сотрудников КФУ | |
dc.source.id | SCOPUS00218979-2018-124-15-SID85054280182 |