Adiabatic superconducting cells for ultra-low-power artificial neural networks.
We propose the concept of using superconducting quantum interferometers for the implementation of neural network algorithms with extremely low power dissipation. These adiabatic elements are Josephson cells with sigmoid- and Gaussian-like activation functions. We optimize their parameters for application in three-layer perceptron and radial basis function networks.
Lomonosov Moscow State University, Physics Faculty, Moscow, 119991, Russia; Moscow Technical University of Communications and Informatics (MTUCI), Moscow, 111024, Russia.
Andrey E
AE
Schegolev
Lomonosov Moscow State University, Physics Faculty, Moscow, 119991, Russia; Moscow Technical University of Communications and Informatics (MTUCI), Moscow, 111024, Russia; Lomonosov Moscow State University Skobeltsyn Institute of Nuclear Physics, Moscow, 119991, Russia; N.L. Dukhov All-Russia Research Institute of Automatics, 127055, Moscow, Russia; Moscow Institute of Physics and Technology, State University, Dolgoprudniy, Moscow region, Russia.
Nikolay V
NV
Klenov
Lomonosov Moscow State University Skobeltsyn Institute of Nuclear Physics, Moscow, 119991, Russia; Moscow Institute of Physics and Technology, State University, Dolgoprudniy, Moscow region, Russia; Lukin Scientific Research Institute of Physical Problems, Zelenograd, Moscow, 124460, Russia.
Igor I
II
Soloviev
Moscow Technical University of Communications and Informatics (MTUCI), Moscow, 111024, Russia.
Maxim V
MV
Tereshonok
Josephson effect; adiabatic superconductor cells; artificial neural networks; energy efficiency; superconductivity