CNN Acceleration With Hardware-Efficient Dataflow for Super-Resolution

CNN Acceleration With Hardware-Efficient Dataflow for Super-Resolution

The convolutional neural network (CNN)-based super-resolution (SR) has shown outstanding performance in the field of computer vision. The implementation of inference hardware for CNN-based SR has suffered from the intensive computation with severely unbalanced computation load among layers. Various...

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Bibliographic Details
Main Authors: Sumin Lee, Sunghwan Joo, Hong Keun Ahn, Seong-Ook Jung
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Convolutional neural network
dataflow
field-programmable gate array
inference hardware
4K ultrahigh definition
process element utilization
Online Access:https://ieeexplore.ieee.org/document/9223656/

Internet

https://ieeexplore.ieee.org/document/9223656/

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