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arXiv · 1812.06426

Auto-tuning Neural Network Quantization Framework for Collaborative Inference Between the Cloud and Edge

Guangli Li·Lei Liu·Xueying Wang·Xiao Dong·Peng Zhao·Xiaobing Feng
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Abstract

Recently, deep neural networks (DNNs) have been widely applied in mobile intelligent applications. The inference for the DNNs is usually performed in the cloud. However, it leads to a large overhead of transmitting data via wireless network. In this paper, we demonstrate the advantages of the cloud-edge collaborative inference with quantization. By analyzing the characteristics of layers in DNNs, an auto-tuning neural network quantization framework for collaborative inference is proposed. We study the effectiveness of mixed-precision collaborative inference of state-of-the-art DNNs by using ImageNet dataset. The experimental results show that our framework can generate reasonable network partitions and reduce the storage on mobile devices with trivial loss of accuracy.

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Guangli Li, Lei Liu, Xueying Wang, Xiao Dong, Peng Zhao, Xiaobing Feng. 2018-12-16. Auto-tuning Neural Network Quantization Framework for Collaborative Inference Between the Cloud and Edge. https://arxiv.org/abs/1812.06426

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