A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks Deep neural networks (DNNs) have achieved significant success in a variety of real world applications, i.e., image classification. However, tons of parameters in the networks restrict the efficiency of neural networks due to the large model size and the intensive computation. To address this issue, various approximation techniques have been investigated, which seek for a light weighted network with little performance degradation in exchange of smaller model size or faster inference. Both lowrankness and sparsity are appealing properties for the network approximation. In this paper we propose a unified framework to compress the convolutional neural networks (CNNs) by combining these two properties, while taking the nonlinear activation into consideration. Each layer in the network is approximated by the sum of a structured sparse component and a lowrank component, which is formulated as an optimization problem. Then, an extended version of alternating direction method of multipliers (ADMM) with guaranteed convergence is presented to solve the relaxed optimization problem. Experiments are carried out on VGG16, AlexNet and GoogLeNet with large image classification datasets. The results outperform previous work in terms of accuracy degradation, compression rate and speedup ratio. The proposed method is able to remarkably compress the model (with up to 4.9X reduction of parameters) at a cost of little loss or without loss on accuracy.

Optimization Strategies in Quantized Neural Networks: A Review Deep neural networks(DNNs) have achieved great success in many realworld applications, but they also had some drawbacks such as considerable storage requirement, large computational power consumption and delay for training and inference, making it impracticable to deploy stateoftheart models into embedded systems and portable devices. Thus, the demand of compressing DNNs has been taken into consideration. In this paper, we focus on quantized neural networks, which is one scheme of compressing DNNs. At first we introduce some baseline works in quantized neural networks and then give a review on optimization ways used in quantizing neural networks. In our perspective, these methods fall into two categories: minimizing quantization error and minimizing loss function. Specialized introduction for each category follows after baseline works. We also make some comments to each category and some methods. Finally, we discuss on some possible directions of this area and make a conclusion.