Bidirectional Non-Filamentary RRAM as an Analog Neuromorphic Synapse, Part II: Impact of Al/Mo/Pr<sub>0.7</sub>Ca<sub>0.3</sub>MnO<sub>3</sub> Device Characteristics on Neural Network Training Accuracy

Bidirectional Non-Filamentary RRAM as an Analog Neuromorphic Synapse, Part II: Impact of Al/Mo/Pr<sub>0.7</sub>Ca<sub>0.3</sub>MnO<sub>3</sub> Device Characteristics on Neural Network Training Accuracy

Neuromorphic computing embraces the &#x201C;device history&#x201D; offered by many analog non-volatile memory (NVM) devices to implement the small weight changes computed by a gradient-descent learning algorithm such as backpropagation. Deterministic and stochastic imperfections in the condu...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Alessandro Fumarola, Severin Sidler, Kibong Moon, Junwoo Jang, Robert M. Shelby, Pritish Narayanan, Yusuf Leblebici, Hyunsang Hwang, Geoffrey W. Burr
Formato: article
Lenguaje:EN
Publicado: IEEE 2018
Materias:
Multi-layer neural network
neural network hardware
nonvolatile memory
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Acceso en línea:https://doaj.org/article/36efdf8a930c4875b9b68848f34662b6
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!

Internet

https://doaj.org/article/36efdf8a930c4875b9b68848f34662b6

Ejemplares similares