  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.
