Adiabatic superconducting artificial neural network: Basic cells

Igor I. Soloviev (Corresponding Author), Andrey E. Schegolev, Nikolay V. Klenov, Sergey V. Bakurskiy, Mikhail Yu Kupriyanov, Maxim V. Tereshonok, Anton V. Shadrin, Vasily S. Stolyarov, Alexander A. Golubov

Research output: Contribution to journalArticleAcademicpeer-review

53 Citations (Scopus)
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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.

Original languageEnglish
Article number152113
JournalJournal of Applied Physics
Issue number15
Publication statusPublished - 21 Oct 2018


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