A Locally Distributed Mobile Computing Framework for DNN based Android Applications

In recent years, with the development of deep neural network (DNN), more and more applications (e.g., image classification, target recognition and audio processing) are supported by it. However, the disadvantage of its own large model makes it difficult to apply on resource-constrained devices such as mobile devices. In order to solve this problem, the existing research and technology mainly focus on the DNN model compression and the segmentation migration of the model. The former is generally at the expense of reducing accuracy, and the segmentation of the model has no unified migration tool for the DNN model of different applications. In this work, we propose a universal neural network layer segmentation tool, which enables the trained DNN model to be migrated, and migrates the segmentation layer to the nodes in the current network in accordance with the dynamic optimal allocation algorithm proposed in this paper. The experimental results show that the tool can adapt to various neural networks with different structures and perform optimal allocation of layers through algorithm. When the number of working nodes increases from 1 to 5, this method can speed up DNN 2-2.5 times, and shows a good acceleration effect.