Towards efficient convolutional neural network for embedded hardware via multi-dimensional pruning
In this paper, we propose TECO, a multi-dimensional pruning framework to collaboratively prune the three dimensions (depth, width, and resolution) of convolutional neural networks (CNNs) for better execution efficiency on embedded hardware. In TECO, we first introduce a two-stage importance evaluati...
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Main Authors: | Kong, Hao, Liu, Di, Luo, Xiangzhong, Huai, Shuo, Subramaniam, Ravi, Makaya, Christian, Lin, Qian, Liu, Weichen |
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Other Authors: | School of Computer Science and Engineering |
Format: | Conference or Workshop Item |
Language: | English |
Published: |
2023
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/167489 |
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Institution: | Nanyang Technological University |
Language: | English |
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