A flexible and fast digital twin for RRAM systems applied for training resilient neural networks

Abstract Resistive Random Access Memory (RRAM) has gained considerable momentum due to its non-volatility and energy efficiency. Material and device scientists have been proposing novel material stacks that can mimic the “ideal memristor” which can deliver performance, energy efficiency, reliability...

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Principais autores:	Markus Fritscher, Simranjeet Singh, Tommaso Rizzi, Andrea Baroni, Daniel Reiser, Maen Mallah, David Hartmann, Ankit Bende, Tim Kempen, Max Uhlmann, Gerhard Kahmen, Dietmar Fey, Vikas Rana, Stephan Menzel, Marc Reichenbach, Milos Krstic, Farhad Merchant, Christian Wenger
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	Nature Portfolio 2024-10-01
סדרה:	Scientific Reports
נושאים:	RRAM Modelling GPU Digital twin ANN FAT
גישה מקוונת:	https://doi.org/10.1038/s41598-024-73439-z
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