Hardware implementation of memristor-based artificial neural networks

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not...

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Veröffentlicht in:	Nature communications Jg. 15; H. 1; S. 1974 - 40
Hauptverfasser:	Aguirre, Fernando, Sebastian, Abu, Le Gallo, Manuel, Song, Wenhao, Wang, Tong, Yang, J. Joshua, Lu, Wei, Chang, Meng-Fan, Ielmini, Daniele, Yang, Yuchao, Mehonic, Adnan, Kenyon, Anthony, Villena, Marco A., Roldán, Juan B., Wu, Yuting, Hsu, Hung-Hsi, Raghavan, Nagarajan, Suñé, Jordi, Miranda, Enrique, Eltawil, Ahmed, Setti, Gianluca, Smagulova, Kamilya, Salama, Khaled N., Krestinskaya, Olga, Yan, Xiaobing, Ang, Kah-Wee, Jain, Samarth, Li, Sifan, Alharbi, Osamah, Pazos, Sebastian, Lanza, Mario
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 04.03.2024 Nature Publishing Group Nature Portfolio
Schlagworte:	639/166/987 639/301/1005/1007 639/925/927/1007 Artificial intelligence Artificial neural networks CMOS Data communication Deep learning Energy efficiency Hardware Humanities and Social Sciences Machine learning Memory devices Memristors multidisciplinary Neural networks Parallel processing Performance evaluation Performance measurement Power management Review Review Article Science Science (multidisciplinary)
ISSN:	2041-1723, 2041-1723
Online-Zugang:	Volltext
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