Smart scissor: coupling spatial redundancy reduction and CNN compression for embedded hardware

Smart scissor: coupling spatial redundancy reduction and CNN compression for embedded hardware

Scaling down the resolution of input images can greatly reduce the computational overhead of convolutional neural networks (CNNs), which is promising for edge AI. However, as an image usually contains much spatial redundancy, e.g., background pixels, directly shrinking the whole image will lose impo...

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Bibliographic Details
Main Authors: Kong, Hao, Liu, Di, Huai, Shuo, Luo, Xiangzhong, Liu, Weichen, Subramaniam, Ravi, Makaya, Christian, Lin, Qian
Other Authors: School of Computer Science and Engineering
Format: Conference Paper
Language:English
Published: 2023
Subjects:
Engineering::Computer science and engineering
Convolutional Neural Networks
Online Access:https://hdl.handle.net/10356/164833

Internet

https://hdl.handle.net/10356/164833

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