As an essential step in quality control, fabric defect detection plays an important role in the textile manufacturing industry. The traditional manual detection method is inaccurate and incurs a high cost; as a result, it is gradually being replaced by deep learning algorithms based on cloud computing. However, a high data transmission latency between end devices and the cloud has a significant impact on textile production efficiency. In contrast, edge computing, which provides services near end devices by deploying network, computing and storage facilities at the edge of the Internet, can effectively solve the abovementioned problem. In this paper, we propose a deep learning-based fabric defect detection method for edge computing scenarios. First, this study modifies the structure of DenseNet to better suit a resource-constrained edge computing scenario. To better assess the proposed model, an optimized cross-entropy loss function is also formulated. Afterwards, six feasible expansion schemes are utilized to enhance the dataset according to the characteristics of various defects in fabric samples. To balance the distribution of samples, proportions of various defect types are used to determine the number of enhancements. Finally, a fabric defect detection system is established to test the performance of the optimized model used on edge devices in a real-world textile industry scenario. Experimental results demonstrate that, compared with the conventional convolutional neural network (CNN), the proposed optimized model attains an average improvement of 18% in the area under the curve (AUC) metric for 11 defects. Data transmission is reduced by approximately 50%, and latency is reduced by 32% in the Cambricon 1H8 platform compared with a cloud platform.

ResNets are widely used in the intrusion detection system (IDS) of software-defined industrial network to construct accurate intelligence detection of network attacks. However, the IDS based on ResNets has a long detecting interval because of the fine-grained operator and intermediate outcomes of the multi-branch architecture of ResNets. To address this problem, in this article, we propose Juggler-ResNet with a fusible residual structure that preserves the feature extraction ability of the residual structure and enables equivalent transformation to linear topology to support low latency inference service in the industrial application (e.g., malicious network behavior detection, fault diagnosis, etc.). First, we propose a fusible multibranch residual structure to avoid gradient vanishing problems in the training phase. Second, we convert it to linear-topology by using a set of equivalent fusion operators. Finally, the linear-topology model is deployed to accelerate inference speed. Our experimental results on CIFAR-10 and CIFAR-100 show that fusible residual structure can achieve 2.08-4.3x acceleration with state-of-the-art level accuracy performance.

Real-time performance is the primary requirement for edge computing systems. However, with the surge in data volume and the growing demand for computing power, a computing framework consisting solely of CPUs is no longer competent. As a result, CPU+ heterogeneous architecture using accelerators to improve edge computing systems’ computing capacity has received great attention. The type of accelerators determines the performance of the edge computing system largely. The accelerators include Graphics Processing Unit (GPU), Application Specific Integrated Circuit (ASIC) and Field Programmable Gate Array (FPGA). FPGAs with its reconfigurability and high energy efficiency are widely used in many edge computing scenarios. Nontheless, the performance depends also on the scheduling efficiency between software tasks on CPUs and hardware tasks on FPGAs. Unfortunately, the existing strategies have not fully exploited the differences between hardware and software tasks, thus resulting in low scheduling efficiency. This paper proposes a task scheduling framework on the Dynamic Partial Reconfiguration (DPR) platform. We take full account of the characteristics of task switching overhead and predictable execution time of hardware tasks in DPR, and reduce the number of task-switching times and active tasks in the system, thus improving the scheduling efficiency. We conduct a set of experiments on the Zynq platform to verify the proposed framework. Experimental results demonstrate that when the execution time of the accelerator exceeds the reconfiguration cost by an order of magnitude, the efficiencies of all the cases are more than 98%, and the efficiencies can reach 90%-98% in the same order of magnitude.

The cloud–edge collaborative inference requires efficient scheduling of artificial intelligence (AI) tasks to the appropriate edge intelligence devices. Gls DNN inference latency has become a vital basis for improving scheduling efficiency. However, edge devices exhibit highly heterogeneous due to the differences in hardware architectures, computing power, etc. Meanwhile, the diverse deep neural networks (DNNs) are continuing to iterate over time. The diversity of devices and DNNs introduces high computational costs for measurement methods, while invasive prediction methods face significant development efforts and application limitations. In this article, we propose and develop Ace-Sniper, a scheduling framework with DNN inference latency modeling on heterogeneous devices. First, to address the device heterogeneity, a unified hardware resource modeling (HRM) is designed by considering the platforms as black-box functions that output feature vectors. Second, neural network similarity (NNS) is introduced for feature extraction of diverse and frequently iterated DNNs. Finally, with the results of HRM and NNS as input, the performance characterization network is designed to predict the latencies of the given unseen DNNs on heterogeneous devices, which can be combined into most time-based scheduling algorithms. Experimental results show that the average relative error of DNN inference latency prediction is 11.11%, and the prediction accuracy reaches 93.2%. Compared with the nontime-aware scheduling methods, the average waiting time for tasks is reduced by 82.95%, and the platform throughput is improved by 63% on average.

Edge agents, represented by socially-aware robots and autonomous vehicles, have gradually been integrated into human society. The safety navigation system in interactive scenes is of great importance to them. The key of this system is that the edge agent has the ability to predict the pedestrian trajectory in the dynamic scene, so as to avoid collision. However, predicting pedestrian trajectories in dynamic scenes is not an easy task, because it is necessary to comprehensively consider the spatial-temporal structure of human-environment interaction, visual attention, and the multi-modal behavior of human walking. In this paper, a scalable spatial-temporal graph generation adversarial network architecture (STG-GAN) is introduced, which can comprehensively consider the influence of human-environment interaction and generate a reasonable multi-modal prediction trajectory. First, we use LSTM nodes to flexibly transform the spatial-temporal graph of human-environment interactions into feed-forward differentiable feature coding, and innovatively propose the global node to integrate scene context information. Then, we capture the relative importance of global interactions on pedestrian trajectories through scaled dot product attention, and use recurrent sequence modeling and generative adversarial network architecture for common training, so as to generate reasonable pedestrian future trajectory distributions based on rich mixed features. Experiments on public data sets show that STG-GAN is superior to previous work in terms of accuracy, reasoning speed and rationality of trajectory prediction.

With the rapid development of the Internet of Things (IoT) in recent years, the popularization of different segments of IoT has emerged. The IoT technology is slowly evolving toward intelligence, convenience, low power consumption, large connectivity, and wide coverage. This evolution is significantly attributed to the emergence of Narrowband of Internet of Thing (NB-IoT). NB-IoT is an emerging technology with wide-coverage, large-connection, low-power consumption, and low-costs. The intelligent system based on NB-IoT, an important branch of the IoT field, has various functions and is widely used. However, the intelligent system based on NB-IoT also generates reliability problems, caused by hardware, software, and networks. Therefore, this study deeply evaluates the impact of the NB-IoT network on the reliability of IoT intelligent systems. We simulated the real-world scenario by using the ns-3 simulator, and then carried out several data transmission experiments and recorded the relevant data of transmission performance indicators, finally calculated and analyzed the reliability results by using quantitative reliability indicators. The three main experiments respectively change the transmission distance of the NB-IoT signal, increase the access amount of the NB-IoT signal in the system, and increase the obstacles in the transmission path. The experimental results are represented by the signal-to-interference ratio (SINR), throughput rate, packet loss rate, and correct rate of the received data block and loss. Finally, we verify the impact of the NB-IoT network on system reliability on the basis of the experimental results.