Algorithm for Training Neural Networks on Resistive Device Arrays

Hardware architectures composed of resistive cross-point device arrays can provide significant power and speed benefits for deep neural network training workloads using stochastic gradient descent (SGD) and backpropagation (BP) algorithm. The training accuracy on this imminent analog hardware, howev...

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Detalles Bibliográficos
Autores principales: Gokmen, Tayfun, Haensch, Wilfried
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7054461/
https://www.ncbi.nlm.nih.gov/pubmed/32174807
http://dx.doi.org/10.3389/fnins.2020.00103

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7054461/
https://www.ncbi.nlm.nih.gov/pubmed/32174807
http://dx.doi.org/10.3389/fnins.2020.00103

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