This paper aims to effectively utilize the vast amounts of data generated by data centers, which are used to support fault diagnosis and early warning functions. Due to the large volume and complexity of data, as well as the semantic relationship among data, in this paper, we adopt knowledge graph technology to extract, fuse, process, and update the knowledge in data centers. Then, we provide a feasible method for constructing a knowledge graph for fault diagnosis and early warning in a data center by describing the correlation of data, reasoning and analyzing the data on a reasonable basis. In addition, we also discuss how knowledge is represented.
In recent years, quantizing the weights of a deep neural network draws increasing attention in the area of network compression. An efficient and popular way to quantize the weight parameters is to replace a filter with the product of binary values and a real-valued scaling factor. However, the quantization error of such binarization method raises as the number of a filter's parameter increases. To reduce quantization error in existing network binarization methods, we propose group binary weight networks (GBWN), which divides the channels of each filter into groups and every channel in the same group shares the same scaling factor. We binarize the popular network architectures VGG, ResNet and DesneNet, and verify the performance on CIFAR10, CIFAR100, Fashion-MNIST, SVHN and ImageNet datasets. Experiment results show that GBWN achieves considerable accuracy increment compared to recent network binarization methods, including BinaryConnect, Binary Weight Networks and Stochastic Quantization Binary Weight Networks.