Compression of Conditional Deep Learning Network for Fast and Low Power Mobile Applications

CDLN(Conditional Deep Learning Network)is a structure of convolution neural network with multiple classifiers. CDLN could improve the speed for the task of classification while the module of the network is still too large for mobile devices. To address this issue, a method for compressing CDLN, which is named one-shot whole network compression scheme. In the experiments, the module size and time cost are significantly reduced while the accuracy of the network losses a little. 1. Convolutional Neural Network Convolutional Neural Network (CNN), which is widely applied in computer vision, is a representative method of deep learning due to its excellent learning ability for high dimensional data feature[1]. Recent years, with the emergence of related learning techniques, optimization techniques and hardware technology, convolution neural network has explosively developed[2]. ImageNet Large Scale Visual Recognition Challenge (ILSVRC) is a standard challenge for large-scale recognition. CNN has been widely used in classification activities of ImageNet and has achieved excellent classification results[3]. From the 8-layer AlexNet[4] to the 19-layer VGGNet[5] and the 152-layer ResNet[6], CNN is going deeper and deeper and the top-5 error reduces from 15.3% to 6.8% and 3.57%. However, the cost time and energy of forward propagation when training the network increase drastically[7]. For example, the running time of VGGNet is 20 times of AlexNet when performing classification tasks in the same dataset and experimental condition[8]. In addition, engineers and developers usually need to take time cost in concern in the context of industrial and commercial applications[9]. For instance, the online search engines need to response rapidly, and the cloud service needs to deal with thousands of pictures per second. Otherwise, the applications for scene recognition on smart phones and portable devices, which are lack of powerful ability for computing, need to response quickly. In 2011, Vanhoucke et al[10] research the method of the code optimization to speed up execution of CNN to reduce the runtime of the network. In 2013, Mathieu et al[11] convolute the convolution value as the dot product of the Fourier domain, when repeat the use of the same transform feature map, for the aid of reducing the runtime. In 2015, Kim et al[12] apply Tucker decomposition to extract the shared information between the convolution layer and the execution rank selection. This method reduces the number of network parameters for fast inference at the expense of the accuracy. In 2016, Panda p et al[13] propose a Conditional Deep Learning Network(CDLN) which adding extra linear classifiers behind the convolution layers. Through monitoring the output of the liner classifiers the ones that are easy to classify is classified in advance and exit the network for fast inference. The structure of the network is modified in CDLN which is a novel way. In this paper, a method for compressing the module of the network is applied to compress the CDLN for fast and low power mobile applications. 2nd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2017) Copyright © 2017, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Computer Science Research, volume 70