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Superconducting base elements for brain-like artificial neura network

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dc.contributor.author SIDORENKO, Anatolie
dc.date.accessioned 2024-12-05T16:56:49Z
dc.date.available 2024-12-05T16:56:49Z
dc.date.issued 2024
dc.identifier.citation SIDORENKO, Anatolie. Superconducting base elements for brain-like artificial neura network. In: Electronics, Communications and Computing (IC ECCO-2024): The conference program and abstract book: 13th intern. conf., Chişinău, 17-18 Oct. 2024. Technical University of Moldova. Chişinău: Tehnica-UTM, 2024, p. 34. ISBN 978-9975-64-480-8 (PDF). en_US
dc.identifier.isbn 978-9975-64-480-8
dc.identifier.uri http://repository.utm.md/handle/5014/28716
dc.description Only Abstract en_US
dc.description.abstract Increasing energy consumption of computers with von-Neumann architecture rose the necessity of energy efficiency and the radically reduction of the power consumption as a crucial parameter constraining the advance of supercomputers. The promising solution is development of the non-von Neumann computers with brain-like architecture – the Artificial Neural Networks (ANN) based on superconducting elements with two main parts: nonlinear switch similar to the neuron, and linear connecting elements similar to synapse. We present results of design and investigation of artificial neurons, based on superconducting spin valves, and superconducting synapses, based on layered hybrid nanostructures superconductor-ferromagnet. en_US
dc.language.iso en en_US
dc.publisher Technical University of Moldova en_US
dc.relation.ispartofseries Electronics, Communications and Computing (IC ECCO-2024): 13th intern. conf., 17-18 Oct. 2024;
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject neura network en_US
dc.subject superconducting en_US
dc.title Superconducting base elements for brain-like artificial neura network en_US
dc.type Article en_US


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  • 2024
    The 13th International Conference on Electronics, Communications and Computing (IC ECCO-2024)

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