| Autor: |
Andrey E. Schegolev, Nikolay V. Klenov, Sergey V. Bakurskiy, Igor I. Soloviev, Mikhail Yu. Kupriyanov, Maxim V. Tereshonok, Anatoli S. Sidorenko |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Beilstein Journal of Nanotechnology, Vol 13, Iss 1, Pp 444-454 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2190-4286 |
| DOI: |
10.3762/bjnano.13.37 |
| Popis: |
The hardware implementation of signal microprocessors based on superconducting technologies seems relevant for a number of niche tasks where performance and energy efficiency are critically important. In this paper, we consider the basic elements for superconducting neural networks on radial basis functions. We examine the static and dynamic activation functions of the proposed neuron. Special attention is paid to tuning the activation functions to a Gaussian form with relatively large amplitude. For the practical implementation of the required tunability, we proposed and investigated heterostructures designed for the implementation of adjustable inductors that consist of superconducting, ferromagnetic, and normal layers. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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