Energy Efficient Superconducting Neural Networks for High-Speed Intellectual Data Processing Systems

Klenov, N.V.; Schegolev, A.E.; Soloviev, I.I.; Bakurskiy, S.V.; Tereshonok, M.V.

Авторы: Klenov Nikolay V., Schegolev Andrey E., Soloviev Igor I., Bakurskiy Sergey V., Tereshonok Maxim V.
Журнал: IEEE Transactions on Applied Superconductivity
Том: 28
Номер: 7
Год издания: 2018
Издательство: Institute of Electrical and Electronics Engineers
Местоположение издательства: Piscataway, NJ, United States
Первая страница: 1301006-1
Последняя страница: 1301006-6
DOI: 10.1109/TASC.2018.2836903
Аннотация: We present the results of circuit simulations for the adiabatic flux-operating neuron. The proposed cell with a one-shot calculation of activation function is based on a modified single-junction superconducting quantum interferometer. In comparison, functionally equivalent elements of the artificial neural network in the semiconductor-based implementations consist of approximately 20 transistors. Also in the article, we present the connecting synapse based on the adiabatic quantum flux parametron. These neurons and synapses allow constructing artificial neural networks with a magnetic representation of information in the form of direction and/or magnitude of the magnetic flux in the superconducting circuit. We discuss the dissipation of energy during operations in the frame of the proposed concept. This value in superconducting neurons and synapses with sub-nanosecond timescale can be reduced down to 10 aJ and 0.1 aJ respectively. The use of adiabatic superconducting logic circuits in our approach promises compatibility with superconducting quantum information processing systems.
Добавил в систему: Кленов Николай Викторович

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Energy Efficient Superconducting Neural Networks for High-Speed Intellectual Data Processing Systemsстатья

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