FitCNN: A cloud-assisted lightweight convolutional neural network framework for mobile devices
Shiming Li, Duo Liu, Chaoneng Xiang, Jianfeng Liu, Yingjian Ling, Tianjun Liao, Liang Liang · 2017
Recently convolutional neural networks (CNNs) have essentially reached the state-of-the-art accuracies in image classification and recognition. CNNs are usually deployed in server side or cloud to handle tasks collected from mobile devices, such as smartphones, wearable devices, unmanned systems and so on. However, significant data transmission overhead and privacy issues have made it necessary to use CNNs directly in device side. Nevertheless, the statically trained model deployed on mobile devices cannot effectively handle the unknown data and objects in new environments, which could lead to low accuracy and unsatisfied user experience. Hence, it would be crucial to retrain a better model via future unknown data. However, with tremendous computing cost and memory usage, training a CNN on mobile devices with limited hardware resources is intolerable in practical. To solve this issue, by using the power of cloud is a promising solution to assist mobile devices to train a deep neural network. Therefore, this paper proposes a cloud-assisted lightweight CNN framework, named FitCNN, with incremental learning and low data transmission, to deploy CNNs on mobile devices and make them smarter. To reduce the data transmission during incremental learning, we propose a strategy to selectively upload the data with high learning value, and develop an extracting strategy to choose light weights of the new CNN model trained on the cloud to update the old one on devices. Experimental results show that the selectively uploading strategy can reduce 39.4% uploading transmission based on a certain dataset, and the extracting weights strategy reduces by more than 60% updating transmission with multiple CNNs and datasets.