Material to system-level benchmarking of CMOS-integrated RRAM with ultra-fast switching for low power on-chip learning

Material to system-level benchmarking of CMOS-integrated RRAM with ultra-fast switching for low power on-chip learning

Abstract Analog hardware-based training provides a promising solution to developing state-of-the-art power-hungry artificial intelligence models. Non-volatile memory hardware such as resistive random access memory (RRAM) has the potential to provide a low power alternative. The training accuracy of...

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Bibliographic Details
Main Authors: Minhaz Abedin, Nanbo Gong, Karsten Beckmann, Maximilian Liehr, Iqbal Saraf, Oscar Van der Straten, Takashi Ando, Nathaniel Cady
Format: Article
Language:English
Published: Nature Portfolio 2023-09-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-023-42214-x

Internet

https://doi.org/10.1038/s41598-023-42214-x

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