Asymmetrical Training Scheme of Binary-Memristor-Crossbar-Based Neural Networks for Energy-Efficient Edge-Computing Nanoscale Systems

For realizing neural networks with binary memristor crossbars, memristors should be programmed by high-resistance state (HRS) and low-resistance state (LRS), according to the training algorithms like backpropagation. Unfortunately, it takes a very long time and consumes a large amount of power in tr...

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Detalles Bibliográficos
Autores principales: Pham, Khoa Van, Tran, Son Bao, Nguyen, Tien Van, Min, Kyeong-Sik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412588/
https://www.ncbi.nlm.nih.gov/pubmed/30791655
http://dx.doi.org/10.3390/mi10020141

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412588/
https://www.ncbi.nlm.nih.gov/pubmed/30791655
http://dx.doi.org/10.3390/mi10020141

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