Robust high-dimensional memory-augmented neural networks
The implementation of memory-augmented neural networks using conventional computer architectures is challenging due to a large number of read and write operations. Here, Karunaratne, Schmuck et al. propose an architecture that enables analog in-memory computing on high-dimensional vectors at accurac...
Guardado en:
Autores principales: | Geethan Karunaratne, Manuel Schmuck, Manuel Le Gallo, Giovanni Cherubini, Luca Benini, Abu Sebastian, Abbas Rahimi |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/b553751b28d34003af781f5a72f0719a |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Accurate deep neural network inference using computational phase-change memory
por: Vinay Joshi, et al.
Publicado: (2020) -
Winsorization for Robust Bayesian Neural Networks
por: Somya Sharma, et al.
Publicado: (2021) -
Temporal correlation detection using computational phase-change memory
por: Abu Sebastian, et al.
Publicado: (2017) -
An empirical survey of data augmentation for time series classification with neural networks.
por: Brian Kenji Iwana, et al.
Publicado: (2021) -
An Efficient and Robust Star Identification Algorithm Based on Neural Networks
por: Bendong Wang, et al.
Publicado: (2021)