Sparse-PE: A Performance-Efficient Processing Engine Core for Sparse Convolutional Neural Networks
Sparse convolutional neural network (CNN) models reduce the massive compute and memory bandwidth requirements inherently present in dense CNNs without a significant loss in accuracy. Sparse CNNs, however, present their own set of challenges including non-linear data accesses and complex design of CN...
Guardado en:
Autores principales: | Mahmood Azhar Qureshi, Arslan Munir |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
IEEE
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/cb34629c94244243822ec50d59d8eb02 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Hyperspectral Unmixing Based on Spectral and Sparse Deep Convolutional Neural Networks
por: Lulu Wan, et al.
Publicado: (2021) -
Power Efficient Design of High-Performance Convolutional Neural Networks Hardware Accelerator on FPGA: A Case Study With GoogLeNet
por: Ahmed J. Abd El-Maksoud, et al.
Publicado: (2021) -
Sparse and dense matrix multiplication hardware for heterogeneous multi-precision neural networks
por: Jose Nunez-Yanez, et al.
Publicado: (2021) -
Multisensor Land Cover Classification With Sparsely Annotated Data Based on Convolutional Neural Networks and Self-Distillation
por: Yawogan Jean Eudes Gbodjo, et al.
Publicado: (2021) -
Combining Accuracy and Plasticity in Convolutional Neural Networks Based on Resistive Memory Arrays for Autonomous Learning
por: Stefano Bianchi, et al.
Publicado: (2021)