Resource-Aware Saliency-Guided Differentiable Pruning for Deep Neural Networks

Author:

Kallakuri Uttej1ORCID,Humes Edward2ORCID,Mohsenin Tinoosh1ORCID

Affiliation:

1. Electrical and Computer Engineering, Johns Hopkins University, USA

2. Computer Science, University of Maryland, Baltimore County, USA

Publisher

ACM

Reference26 articles.

1. The generalization-stability tradeoff in neural network pruning;Bartoldson Brian;Advances in Neural Information Processing Systems,2020

2. Shih-Kang Chao, Zhanyu Wang, Yue Xing, and Guang Cheng. 2020. Directional pruning of deep neural networks. Advances in neural information processing systems 33 (2020), 13986–13998.

3. Hongrong Cheng, Miao Zhang, and Javen Qinfeng Shi. 2023. A survey on deep neural network pruning-taxonomy, comparison, analysis, and recommendations. arXiv preprint arXiv:2308.06767 (2023).

4. Kaiyuan Guo, Lingzhi Sui, Jiantao Qiu, Jincheng Yu, Junbin Wang, Song Yao, Song Han, Yu Wang, and Huazhong Yang. 2017. Angel-eye: A complete design flow for mapping CNN onto embedded FPGA. IEEE transactions on computer-aided design of integrated circuits and systems 37, 1 (2017), 35–47.

5. Song Han, Huizi Mao, and William J Dally. 2015. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015).

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