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A. E. Schegolev
Researcher at Moscow State University
Publications - 24
Citations - 215
A. E. Schegolev is an academic researcher from Moscow State University. The author has contributed to research in topics: Josephson effect & Computer science. The author has an hindex of 6, co-authored 15 publications receiving 116 citations.
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Journal ArticleDOI
Adiabatic superconducting artificial neural network: Basic cells
Igor I. Soloviev,Igor I. Soloviev,Igor I. Soloviev,A. E. Schegolev,Nikolay V. Klenov,S. V. Bakurskiy,S. V. Bakurskiy,S. V. Bakurskiy,Mikhail Yu. Kupriyanov,M. V. Tereshonok,A. V. Shadrin,Vasily S. Stolyarov,Alexander A. Golubov +12 more
TL;DR: In this paper, the authors consider adiabatic superconducting cells operating as an artificial neuron and synapse of a multilayer perceptron and their compact circuits contain just one and two Josephson junctions, respectively.
Journal ArticleDOI
Adiabatic superconducting cells for ultra-low-power artificial neural networks
TL;DR: This work proposes the concept of using superconducting quantum interferometers for the implementation of neural network algorithms with extremely low power dissipation using adiabatic elements that are Josephson cells with sigmoid- and Gaussian-like activation functions.
Journal ArticleDOI
Adiabatic Superconducting Artificial Neural Network: Basic Cells.
Igor I. Soloviev,A. E. Schegolev,Nikolay V. Klenov,S. V. Bakurskiy,M. Yu. Kupriyanov,M. V. Tereshonok,A. V. Shadrin,Vasily S. Stolyarov,Alexandre Avraamovitch Golubov +8 more
TL;DR: In this article, the authors considered adiabatic superconducting cells operating as an artificial neuron and synapse of a multilayer perceptron and their compact circuits contain just one and two Josephson junctions, respectively.
Journal ArticleDOI
Analytical derivation of DC SQUID response
Igor I. Soloviev,Igor I. Soloviev,Nikolay V. Klenov,A. E. Schegolev,S. V. Bakurskiy,S. V. Bakurskiy,M. Yu. Kupriyanov,M. Yu. Kupriyanov,M. Yu. Kupriyanov +8 more
TL;DR: In this paper, the voltage and current response formation in DC superconducting quantum interference device (SQUID) with overdamped Josephson junctions in resistive and super-conducting state in the context of a resistively shunted junction (RSJ) model was considered.
Journal ArticleDOI
Energy Efficient Superconducting Neural Networks for High-Speed Intellectual Data Processing Systems
TL;DR: In this paper, the authors presented the results of circuit simulations for the adiabatic flux-operating neuron and the connecting synapse based on the quantum flux parametron, which allows constructing ANNs with a magnetic representation of information in the form of direction and/or magnitude of the magnetic flux in the superconducting circuit.