We have witnessed the tremendous success of deep neural networks. However, this success comes with the considerable memory and computational costs which make it difficult to deploy these networks directly on resource-constrained embedded systems. To address this problem, we propose TaijiNet, a separable binary network, to reduce the storage and computational overhead while maintaining a comparable accuracy. Furthermore, we also introduce a strategy called partial binarized convolution which binarizes only unimportant kernels to efficiently balance network performance and accuracy. Our approach is evaluated on the CIFAR-10 and ImageNet datasets. The experimental results show that with the proposed TaijiNet, the separable binary versions of AlexNet and ResNet-18 can achieve 26× and 6.4× compression rates with comparable accuracy when comparing with the full-precision versions respectively. In addition, by adjusting the PCA threshold, the xnor version of Taiji-AlexNet improves accuracy by 4-8 percent comparing with other state-of-the-art methods.

In recent years, time-series data have emerged in a variety of application domains, such as wireless sensor networks and surveillance systems. To identify the similarity between time-series data, the Euclidean distance and its variations are common metrics that quantify the differences between time-series data. However, the Euclidean distance is limited by its inability to elastically shift with the time axis, which motivates the development of dynamic time warping (DTW) algorithms. While DTW algorithms have been proven very useful in diversified applications like speech recognition, their efficacy might be seriously affected by the resolution of the time-series data. However, high-resolution time-series data might take up a gigantic amount of main memory and storage space, which will slow down the DTW analysis procedure. This makes the upscaling of DTW analysis more challenging, especially for in-memory data analytics platforms with limited nonvolatile memory space. In this paper, we propose a strategy to downsample time-series data to significantly reduce their size without seriously affecting the precision of the results obtained by DTW algorithms (downsizing without downgrading). In other words, this paper proposes a technique to remove the unimportant details that are largely ignored by DTW algorithms. The efficacy of the proposed technique is verified by a series of experimental studies, where the results are quite encouraging.

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.