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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 4984-4995页

作者： Sun, Shaoqi Xu, Kele Feng, Dawei Ding, Bo National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha410003 China

In recent years, multiagent reinforcement learning (MARL) has demonstrated considerable potential across diverse applications. However, in reinforcement learning environments characterized by sparse rewards, the scarcity of reward signals may give rise to reward conflicts among agents. In these scenarios, each agent tends to compete to obtain limited rewards, deviating from collaborative efforts aimed at achieving collective team objectives. This not only amplifies the learning challenge but also imposes constraints on the overall learning performance of agents, ultimately compromising the attainment of team goals. To mitigate the conflicting competition for rewards among agents in MARL, we introduce the bidirectional influence and interaction (BDII) MARL framework. This innovative approach draws inspiration from the collaborative ethos observed in human social cooperation, specifically the concept of "sharing joys and sorrows." The fundamental concept behind BDII is to empower agents to share their individual rewards with collaborators, fostering a cooperative rather than competitive behavioral paradigm. This strategic shift aims to resolve the pervasive issue of reward conflicts among agents operating in sparse-reward environments. BDII incorporates two key factors—namely, the Gaussian kernel distance between agents (physical distance) and policy diversity among agents (logical distance). The two factor collectively contribute to the dynamic adjustment of reward allocation coefficients, culminating in the formation of reward distribution weights. The incorporation of these weights facilitates the equitable sharing of agents’ contributions to rewards, promoting a cooperative learning environment. Through extensive experimental evaluations, we substantiate the efficacy of BDII in addressing the challenge of reward conflicts in MARL. Our research findings affirm that BDII significantly mitigates reward conflicts, ensuring that agents consistently align with the origi

关键词： Reinforcement learning

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A pipelining strategy for accelerating convolution neural networks on ARM CPUs

A pipelining strategy for accelerating convolution neural ne...

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作者： Zhou, Xin Dou, Yong Li, Rongchun Zhang, Peng Liu, Yuntao National Key Laboratory for Parallel and Distribution Processing National University of Defense Technology Changsha China

Convolution is a primary operation in convolution neural networks. The speed of inference is mainly decided by the speed of the convolutional layer. Improving the performance of embedded processors makes it possible to process the inference on embedded devices. In this article, a pipelining strategy of single instruction and multiple data (SIMD) instructions is proposed to finely optimize the process of the 3 × 3 convolution on ARM-based CPUs. We implement the SIMD group to improve the efficiency of the SIMD pipeline. A tiling method is exploited to increase data reuse during the process. An evaluation model is proposed to guide the design of the tiling method and register allocation. The speed of our implementation is 5.18 times of the GNU compiler collection compiled unoptimized version on RK3288. The effect of our optimizing method is measured by a performance profiling tool, the performance information suggests that the pipelining strategy has a significant effect for both normal and depthwise separable convolution. By implementing multithread processing, the speedup achieves 18.3 compared with the single thread unoptimized version. © 2021 John Wiley & Sons Ltd

关键词： Convolution

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CD-Sched: An Automated Scheduling Framework for Accelerating Neural Network Training on Shared Memory CPU-DSP Platforms

CD-Sched: An Automated Scheduling Framework for Accelerating...

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2023 International Conference on Power, Communication, Computing and Networking Technologies, PCCNT 2023

作者： Xiao, Yuanyuan Lai, Zhiquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Processing Computer College National University of Defense Technology Changsha China

ISBN: (纸本)9781450399951

DSP holds significant potential for important applications in Deep Neural Networks. However, there is currently a lack of research focused on shared-memory CPU-DSP heterogeneous chips. This paper proposes CD-Sched, an automated scheduling framework that aims to address this gap. By predicting the latency of operators on both CPU and DSP, CD-Sched automatically schedules the computation of operators to the appropriate computing device. This scheduling optimization accelerates the computation of individual operators and ultimately improves the overall training time of neural networks. In end-to-end training tasks, CD-Sched can significantly reduce the overall training time, with an average reduction of approximately 10.77%. © 2023 ACM.

关键词： Deep neural networks

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Controllable Template Generation for Document-level Event Extraction 4

Controllable Template Generation for Document-level Event Ex...

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4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Fang, Quntian Liu, Feng Huang, Zhen Guo, Zhenliang Wang, Changjian Li, Dongsheng Hu, Minghao National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha China Academy of Military Sciences Information Research Center Beijing China

ISBN: (纸本)9798350394375

Document-level event extraction task has achieved significant progress based on template generation methods. However, there is no reasonable regulation and restriction in the existing template-based generation methods, which results in the uncontrollability of the generation results. In some scenarios, model generates entities that do not belong to the input text, or generate template content repeatedly. It is determined by the nature of the extraction task and the generation task. To this end, we propose a controllable template generation event extraction model. According to the characteristics of template generation and event extraction tasks, the model devises copy mechanism, inhibition mechanism and rejection mechanism under the appropriately constructed template. Our model achieves state-of-the-art result on MUC-4 dataset, and finally through experimental analysis, it demonstrates the effectiveness of each mechanism we proposed. © 2024 IEEE.

关键词： Extraction

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CD-Sched: An Automated Scheduling Framework for Accelerating Neural Network Training on Shared Memory CPU-DSP Platforms 23

CD-Sched: An Automated Scheduling Framework for Accelerating...

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Proceedings of the 2023 International Conference on Power, Communication, Computing and Networking Technologies

作者： Yuanyuan Xiao Zhiquan Lai Dongsheng Li National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology China National Key Laboratory of Parallel and Distributed Processing National University of Defense Technology China

ISBN: (纸本)9781450399951

关键词： computation offloading

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Accelerating Sample-based GNN Training by Feature Caching on GPUs 7

Accelerating Sample-based GNN Training by Feature Caching on...

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7th IEEE International Conference on Smart Cloud, SmartCloud 2022

作者： He, Yuqi Lai, Zhiquan Ran, Zhejiang Zhang, Lizhi Li, Dongsheng National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing College of Computer Changsha China

ISBN: (数字)9781665451796

ISBN: (纸本)9781665451796

The existing graph neural network (GNN) systems adopt sample-based training on large-scale graphs over multiple GPUs. Although they support large-scale graph training, large data loading overhead is still a bottleneck. In this work, we propose SCGraph, a method that supports GPU high-speed feature caching. We classify the graph vertices sorted by out-degrees. For high out-degree vertices, we set grading caches via different GPUs to increase the overall cache content through NVLink high-speed data transmission between them. For low out-degree vertices, we expand training vertices' neighborhood in advance to regenerate cache. We evaluate SCGraph against two state-of-the-art industrial GNN frameworks, i.e., DGL and PaGraph on two datasets Reddit and ogbn-products. Experimental results show that SCGraph achieves up to 1.83× performance speedup over the state-of-the-art baselines. © 2022 IEEE.

关键词： Graph neural networks

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An Efficient Broadcast Authentication Protocol in Wireless Sensor Networks

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Chinese Journal of Electronics 2023年第2期18卷 368-372页

作者： Xin Zhao Xiaodong Wang Wanrong Yu Xingming Zhou National Key Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha China

Broadcast authentication is a critical security service in wireless sensor networks. A protocol named $\mu\text{TESLA}$ [1] has been proposed to provide efficient authentication service for such networks. However, when applied to applications such as time synchronization and fire alarm in which broadcast messages are sent infrequently, $\mu\text{TESLA}$ encounters problems of wasted key resources and slow message verification. This paper presents a new protocol named GBA (Generalized broadcast authentication), for efficient broadcast authentication in these applications. GBA utilizes the one-way key chain mechanism of $\mu\text{TESLA}$ , but modifies the keys and time intervals association, and changes the key disclosure mechanism according to the message transmission model in these applications. The proposed technique can take full use of key resources, and shorten the message verification time to an acceptable level. The analysis and experiments show that GBA is more efficient and practical than $\mu\text{TESLA}$ in applications with various message transmission models.

关键词： Wireless sensor networks Analytical models Protocols Authentication Synchronization Security Message authentication

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High-performance Network Traffic Classification Based on Graph Neural Network 6

High-performance Network Traffic Classification Based on Gra...

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6th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023

作者： Pang, Bo Fu, Yongquan Ren, Siyuan Jia, Yan Harbin Institute of Technology College of Computer Science and Technology Shenzhen China National University of Defense Technology National Key Laboratory for Parallel and Distributed Processing College of Computer Changsha China Peng Cheng Laboratory Shen Zhen China

ISBN: (纸本)9781665460033

Network traffic classification is crucial for network security and network management and is one of the most important network tasks. Current state-of-the-art traffic classifiers are based on deep learning models to automatically extract features from packet streams. Unfortunately, current approaches fail to effectively combine the structural information of traffic packets with the content features of the packets, resulting in limited classification accuracy. In this paper, we propose a graph neural network model for network traffic classification, which can well perceive the interaction feature of packets in traffic. Firstly, we design a graph structure for packets' flows to hold the interaction information between packets, which embeds both packet contents and sequence relationships into a unified graph. Secondly, we propose a graph neural network framework for graph classification to automatically learn the structural features of the packets' flows together with the packets' features. Extensive evaluation results on real-world traffic data show that the proposed model improves the prediction accuracy of improves the prediction accuracy by 2% to 37% for malicious traffic classification. © 2023 IEEE.

关键词： Graph neural networks

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Jammer Localization for Wireless Sensor Networks

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Chinese Journal of Electronics 2023年第4期20卷 735-738页

作者： SUN Yanqiang WANG Xiaodong ZHOU Xingming National Key Laboratory for Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China

Jamming attack can severely affect the performance of Wireless sensor networks (WSNs) due to the broadcast nature of wireless medium. In order to localize the source of the attacker, we in this paper propose a jammer localization algorithm named as Minimum-circle-covering based localization (MCCL). Comparing with the existing solutions that rely on the wireless propagation parameters, MCCL only depends on the location information of sensor nodes at the border of the jammed region. MCCL uses the plane geometry knowledge, especially the minimum circle covering technique, to form an approximate jammed region, and hence the center of the jammed region is treated as the estimated position of the jammer. Simulation results showed that MCCL is able to achieve higher accuracy than other existing solutions in terms of jammer's transmission range and sensitivity to nodes' density.

关键词： Location awareness Wireless communication Geometry Wireless sensor networks Sensitivity Simulation Jamming

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Prediction of the Cyanobacteria Coverage in Time-series Images based on Convolutional Neural Network 21

Prediction of the Cyanobacteria Coverage in Time-series Imag...

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4th International Conference on Control and Computer Vision, ICCCV 2021

作者： Ye, Xiangyu Lai, Zhiquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Processing Computer College National University of Defense Technology China

ISBN: (纸本)9781450390477

In recent years, the problem of lake eutrophication has become increasingly severe. The monitoring and control of cyanobacteria in lakes are of great significance. The information obtained by existing monitoring methods is relatively lagging, and it is impossible to monitor the sudden outbreak of cyanobacteria in time. Getting cyanobacteria information directly through camera images is a breakthrough. In this paper, after analyzing the characteristics of time series cyanobacteria images, we propose a block prediction scheme based on the CNN model. Experiments show that this method can quickly calculate the coverage of cyanobacteria in the monitoring image in a short time. It can also effectively distinguish cyanobacteria-rich water areas, which significantly facilitates water quality monitoring and cyanobacteria management. We can draw a chart of the changes in the coverage of cyanobacteria by analyzing multi-day time-series images. The chart helps us conduct a short-term water quality analysis to better deal with the outbreak of cyanobacteria. © 2021 ACM.

关键词： Lakes

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