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

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 analog ha...

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Detalles Bibliográficos
Autores principales: Abedin, Minhaz, Gong, Nanbo, Beckmann, Karsten, Liehr, Maximilian, Saraf, Iqbal, Van der Straten, Oscar, Ando, Takashi, Cady, Nathaniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10495451/
https://www.ncbi.nlm.nih.gov/pubmed/37697024
http://dx.doi.org/10.1038/s41598-023-42214-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10495451/
https://www.ncbi.nlm.nih.gov/pubmed/37697024
http://dx.doi.org/10.1038/s41598-023-42214-x

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