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...

Celý popis

Podrobná bibliografie

Hlavní autoři:	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
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Nature Portfolio 2024-10-01
Edice:	Scientific Reports
Témata:	RRAM Modelling GPU Digital twin ANN FAT
On-line přístup:	https://doi.org/10.1038/s41598-024-73439-z
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!