dc.contributor.author | LUPU, Maria | |
dc.contributor.author | KLENOV, Nikolai | |
dc.contributor.author | SOLOVIEV, Igor | |
dc.contributor.author | BAKURSKIY, Sergey | |
dc.contributor.author | BOIAN, Vladimir | |
dc.contributor.author | MALCOCI, Cezar Casian | |
dc.contributor.author | PREPELITA, Andrei | |
dc.contributor.author | ANTROPOV, Evgheni | |
dc.contributor.author | MORARI, Roman | |
dc.contributor.author | SIDORENKO, Anatolie | |
dc.date.accessioned | 2022-12-29T13:14:15Z | |
dc.date.available | 2022-12-29T13:14:15Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | LUPU, Maria, KLENOV, Nikolai, SOLOVIEV, Igor et al. Spintronic Functional Nanostructures for Artificial Neural Network. In: Electronics, Communications and Computing (IC ECCO-2022): 12th intern. conf., 20-21 Oct. 2022, Chişinău, Republica Moldova: conf. proc., Chişinău, 2022, pp. 24-2. | en_US |
dc.identifier.uri | http://repository.utm.md/handle/5014/21898 | |
dc.description | Only Abstract | |
dc.description.abstract | Energy consumption reduction becomes a crucial parameter constraining the advance of supercomputers. The non-von Neumann architectures, first of all – the Artificial Neural Networks (ANN) based on superconducting spintronic elements, seems to be the most promising solution. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Technical University of Moldova | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.subject | supercomputers | en_US |
dc.subject | energy consumption | en_US |
dc.subject | artificial neural networks | en_US |
dc.subject | spintronic elements | en_US |
dc.title | Spintronic Functional Nanostructures for Artificial Neural Network | en_US |
dc.type | Article | en_US |
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