Optimizing Neural Network Efficiency with Hybrid Magnitude-Based and Node Pruning for Energy-efficient Computing in IoT

Author:

Helal Uddin Mohammad1ORCID,Baidya Sabur1ORCID

Affiliation:

1. Computer Science and Engineering, University of Louisville, United States of America

Funder

US National Science Foundation

Publisher

ACM

Reference5 articles.

1. HRank: Filter Pruning Using High-Rank Feature Map

2. Learning Efficient Convolutional Networks through Network Slimming

3. Zhuang Liu , Mingjie Sun , Tinghui Zhou , Gao Huang , and Trevor Darrell . 2018. Rethinking the value of network pruning. arXiv preprint arXiv:1810.05270 ( 2018 ). Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell. 2018. Rethinking the value of network pruning. arXiv preprint arXiv:1810.05270 (2018).

4. Pavlo Molchanov , Stephen Tyree , Tero Karras , Timo Aila , and Jan Kautz . 2016. Pruning convolutional neural networks for resource efficient inference. arXiv preprint arXiv:1611.06440 ( 2016 ). Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz. 2016. Pruning convolutional neural networks for resource efficient inference. arXiv preprint arXiv:1611.06440 (2016).

5. Neural network pruning: An effective way to reduce the initial network for deep learning based human activity recognition

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2. ALRA: Adaptive Low-Rank Approximations for Neural Network Pruning;2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC);2024-07-02

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