A Privacy-Preserving-Oriented DNN Pruning and Mobile Acceleration
Framework
- URL: http://arxiv.org/abs/2003.06513v2
- Date: Thu, 17 Sep 2020 00:45:39 GMT
- Title: A Privacy-Preserving-Oriented DNN Pruning and Mobile Acceleration
Framework
- Authors: Yifan Gong, Zheng Zhan, Zhengang Li, Wei Niu, Xiaolong Ma, Wenhao
Wang, Bin Ren, Caiwen Ding, Xue Lin, Xiaolin Xu, and Yanzhi Wang
- Abstract summary: Weight pruning of deep neural networks (DNNs) has been proposed to satisfy the limited storage and computing capability of mobile edge devices.
Previous pruning methods mainly focus on reducing the model size and/or improving performance without considering the privacy of user data.
We propose a privacy-preserving-oriented pruning and mobile acceleration framework that does not require the private training dataset.
- Score: 56.57225686288006
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Weight pruning of deep neural networks (DNNs) has been proposed to satisfy
the limited storage and computing capability of mobile edge devices. However,
previous pruning methods mainly focus on reducing the model size and/or
improving performance without considering the privacy of user data. To mitigate
this concern, we propose a privacy-preserving-oriented pruning and mobile
acceleration framework that does not require the private training dataset. At
the algorithm level of the proposed framework, a systematic weight pruning
technique based on the alternating direction method of multipliers (ADMM) is
designed to iteratively solve the pattern-based pruning problem for each layer
with randomly generated synthetic data. In addition, corresponding
optimizations at the compiler level are leveraged for inference accelerations
on devices. With the proposed framework, users could avoid the time-consuming
pruning process for non-experts and directly benefit from compressed models.
Experimental results show that the proposed framework outperforms three
state-of-art end-to-end DNN frameworks, i.e., TensorFlow-Lite, TVM, and MNN,
with speedup up to 4.2X, 2.5X, and 2.0X, respectively, with almost no accuracy
loss, while preserving data privacy.
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