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A High-Quality and Efficient Bus-Aware Global Router

Chinese Journal of Electronics 2025年第2期34卷 444-456页

作者： Liu Genggeng Wei Ling Yu Yantao Xu Ning College of Computer and Data Science Fuzhou University Fuzhou 350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou 350108 China School of Information Engineering Wuhan University of Technology Wuhan 430070 China

As advanced technology nodes enter the nanometer era, the complexity of integrated circuit design is increasing, and the proportion of bus in the net is also increasing. The bus routing has become a key factor affecting the performance of the chip. In addition, the existing research does not distinguish between bus and non-bus in the complete global routing process, which directly leads to the expansion of bus deviation and the degradation of chip performance. In order to solve these problems, we propose a high-quality and efficient bus-aware global router, which includes the following key strategies: By introducing the routing density graph, we propose a routing model that can simultaneously consider the routability of non-bus and the deviation value of bus;A dynamic routing resource adjustment algorithm is proposed to optimize the bus deviation and wirelength simultaneously, which can effectively reduce the bus deviation;We propose a layer assignment algorithm consider deviation to significantly reduce the bus deviation of the 3D routing solution;And a depth-first search (DFS)-based algorithm is proposed to obtain multiple routing solutions, from which the routing result with the lowest deviation is selected. Experimental results show that the proposed algorithms can effectively reduce bus deviation compared with the existing algorithms, so as to obtain high-quality 2D and 3D routing solutions considering bus deviation.

关键词： Global routing Layer assignment Bus deviation Physical design Bus routing

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Speculative Decoding with CTC-based Draft Model for LLM Inference Acceleration 38

Speculative Decoding with CTC-based Draft Model for LLM Infe...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Wen, Zhuofan Gui, Shangtong Feng, Yang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences China Key Laboratory of Al Safety Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing China

Inference acceleration of large language models (LLMs) has been put forward in many application scenarios and speculative decoding has shown its advantage in addressing inference acceleration. Speculative decoding usually introduces a draft model to assist the base LLM where the draft model produces drafts and the base LLM verifies the draft for acceptance or rejection. In this framework, the final inference speed is decided by the decoding speed of the draft model and the acceptance rate of the draft provided by the draft model. Currently the widely used draft models usually generate draft tokens for the next several positions in a non-autoregressive way without considering the correlations between draft tokens. Therefore, it has a high decoding speed but an unsatisfactory acceptance rate. In this paper, we focus on how to improve the performance of the draft model and aim to accelerate inference via a high acceptance rate. To this end, we propose a CTC-based draft model which strengthens the correlations between draft tokens during the draft phase, thereby generating higher-quality draft candidate sequences. Experiment results show that compared to strong baselines, the proposed method can achieve a higher acceptance rate and hence a faster inference speed. © 2024 Neural information processing systems foundation. All rights reserved.

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Lightweight-Based Defect Detection for Small Target Insulators

Lightweight-Based Defect Detection for Small Target Insulato...

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Liu, Shuxin Zhang, Lei Shi, Chengcheng Qin, Shuhan Ji, Guanjun Wang, Xiaodi School of Electrical Engineering Shanghai Dianji University Shanghai201306 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Fujian354300 China

ISBN: (纸本)9783031694820

Identifying defects in insulators, especially in small targets, remains a significant challenge due to the diverse types of insulators and complex transmission line backgrounds. This paper aims to enhance the accuracy of defect detection in insulators while minimizing the model’s parameter count for stable power system operation. To achieve this, we propose improvements to the feature fusion mechanism in the YOLOv8 network. These enhancements focus on better integrating multi-scale feature information, particularly emphasizing the detection sensitivity towards small targets. Additionally, these modifications lead to a reduction in the model’s parameter count while improving the detection accuracy of small targets. Furthermore, we introduce the SIOU loss function to the improved model, further enhancing its accuracy. Through experiments, our improved model achieves a mean average precision (mAP) of 89.2%, surpassing YOLOv8n by 2.7%. Remarkably, the model’s parameter count is significantly reduced to 2,796,249, demonstrating a balance between higher model accuracy and fewer parameters. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Insulating materials

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Edge computing based multi-objective equilibrium task unloading of Internet of Vehicles 2

Edge computing based multi-objective equilibrium task unload...

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2nd International Conference on Electronic information Technology, EIT 2023

作者： Wang, Lingjiao Tang, Chen Guo, Hua Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiang Tan University 411105 China School of Automation and Electronic Information Xiang Tan University 411105 China

ISBN: (数字)9781510666627

ISBN: (纸本)9781510666610

The requirements of the Internet of Vehicles on the quality of service (QoS) of computing task offloading are constantly improving. The processing mode of edge computing, which deploys computing, bandwidth and storage resources near the user side, effectively improves the user experience. However, the computing capacity of edge servers is limited. Task offloading requests are overloaded and inefficient due to uneven server loads. Traditional cloud computing consumes a lot of energy and time. To solve the above problem, a KDQN based edge computing balanced task unloading mechanism is proposed: Q learning and convolutional neural network are used to obtain the optimal unloading strategy according to the returns of each step and continuous iterative updates, which reduces the convergence time of task unloading;K-means clustering is used to distribute part of the load of the overloaded server to the server with the closest European distance to achieve server load balancing. The experimental results show that compared to the existing offload mechanism, the new mechanism can reduce the average server energy consumption by 12.8%, the average service delay by 8.2%, and the average overload rate by 29.8%. © 2023 SPIE.

关键词： Quality of service

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A Real-Time Fall Detection System Based on MoveNet and LSTM 1

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Liu, Shuxin Shi, Chengcheng School of Electrical Engineering Shanghai Dianji University Shanghai201306 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Fujian354300 China

ISBN: (数字)9783031702358

ISBN: (纸本)9783031702341

Preventing injuries and deaths caused by the lack of timely assistance to the elderly after a fall is an important issue facing our society. Elderly people are prone to accidents such as falls due to the decline in their physical functions, and once they have fallen, if they do not receive timely assistance, it may lead to more serious consequences and even threaten their lives. Therefore, this study explores a deep learning-based method for fall detection. The objective is to minimize the consequences of such incidents. The study compares 2D pose estimation algorithms and proposes a fall detection algorithm based on MoveNet and Long short-term memory networks. This algorithm utilizes a camera to collect data and extracts skeletal information from videos or images. Long short-term memory is then used to analyze and detect the occurrence of a fall in elderly people. In addition, this study comparatively analyses four 2D pose estimation algorithms, namely OpenPose, BlazePose, PoseNet and MoveNet. The experimental results demonstrate that the MoveNet algorithm exhibits superior real-time performance and accuracy. Through experiments conducted on the UR Fall Detection Dataset, the algorithm achieved high performance. Additionally, the detection frame rate on the CPU reaches up to 33 fps, providing remarkable real-time performance in comparison to other algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Fall detection

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Accelerating Unsupervised Federated Graph Neural Networks via Semi-asynchronous Communication 18th

Accelerating Unsupervised Federated Graph Neural Networks vi...

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18th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2023

作者： Liao, Yuanming Wu, Duanji Lin, Pengyu Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819996360

Graph neural networks have shown excellent performance in many fields owing to their powerful processing ability of graph data. In recent years, federated graph neural network has become a reasonable solution due to the enactment of privacy-related regulations. However, frequent communication between the coordinator and participants in federated graph neural network results in longer model training time and consumes many communication resources. To address this challenge, in this paper, we propose a novel semi-asynchronous federated graph learning communication protocol that simultaneously alleviates the negative impact of stragglers(slow participants) and accelerate the training process in the unsupervised federated graph neural network scenario. First, the weighted enforced synchronization strategy is intended to preserve the information carried by stragglers while preventing their stale models from harming the global model update. Second, the adaptive local update strategy is developed to make the local model of the participant with poor computing performance as close as possible to the global model. Experiments combine federated learning with graph contrastive learning. The results demonstrate that our proposed protocol outperforms the existing protocols in real-world networks. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet protocols

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Polynomial stacked-attention network for nationality classification

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Frontiers of Computer Science 2022年第4期16卷 215-217页

作者： Kunyan Li Jie Zhang Shiguang Shan Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing100190China University of Chinese Academy of Sciences Beijing100049China

1Introduction In recent years,nationality and region inference has atracted more and_more *** anthropological researches indicate that human faces are influenced by genes,and other external factors,e.g-,geographical environment[1].This relevance provides possible solutions for geographical area inference by ***,it remains a challenging task due to the tiny differences on faces for various countries,which is even hard for human beings to tell.

关键词： Polynomial s. network

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Adaptive Multi-Updating Strategy Based Particle Swarm Optimization

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intelligent Automation & Soft computing 2023年第9期37卷 2783-2807页

作者： Dongping Tian Bingchun Li Jing Liu Chen Liu Ling Yuan Zhongzhi Shi School of Computer Science and Technology Kashi UniversityKashi844006China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing100190China

Particle swarm optimization(PSO)is a stochastic computation tech-nique that has become an increasingly important branch of swarm intelligence ***,like other evolutionary algorithms,PSO also suffers from premature convergence and entrapment into local optima in dealing with complex multimodal *** this paper puts forward an adaptive multi-updating strategy based particle swarm optimization(abbreviated as AMS-PSO).To start with,the chaotic sequence is employed to generate high-quality initial particles to accelerate the convergence rate of the ***,according to the current iteration,different update schemes are used to regulate the particle search process at different evolution *** be specific,two different sets of velocity update strategies are utilized to enhance the exploration ability in the early evolution stage while the other two sets of velocity update schemes are applied to improve the exploitation capability in the later evolution *** by the unequal weightage of acceleration coefficients is used to guide the search for the global worst particle to enhance the swarm *** addition,an auxiliary update strategy is exclusively leveraged to the global best particle for the purpose of ensuring the convergence of the PSO ***,extensive experiments on two sets of well-known benchmark functions bear out that AMS-PSO outperforms several state-of-the-art PSOs in terms of solution accuracy and convergence rate.

关键词： Particle swarm optimization local optima acceleration coefficients swarm diversity premature convergence

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Dielectric Resonator Magnetoelectric Dipole Arrays with Low Cross Polarization,Backward Radiation,and Mutual Coupling for MIMO Base Station Applications

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China Communications 2024年第6期21卷 290-304页

作者： Song Simin Da Yiran Qian Bingyi Huang Xiaoyu Chen Xiaoming Li Yingsong Ahmed A.Kishk School of Information and Communications Engineering Xi’an Jiaotong UniversityXi’an 710049China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui UniversityHefei 230039China Department of Electrical and Computer Engineering Concordia UniversityMontrealQC H3G 1M8Canada

Dielectric resonator magnetoelectric dipole(DRMED)arrays with enhanced isolation,reduced cross-polarization,and backward radiation are proposed for base station(BS)*** proposed antenna comprises an elevated dielectric resonator antenna(DRA)on a small metal plate above a sizeable common ground *** DRA is designed in its T Eδ11 mode,acting like a magnetic *** surface current excited by the differential probes flowing on the small ground plane is equivalent to an electric *** these two equivalent dipoles are orthogonal,they have the magnetoelectric dipole characteristics with reduced backward ***,the small ground planes can be treated as decoupling structures to provide a neutralization path to cancel the original coupling path.A linearly-polarized 4-element prototype array was verified experimentally in previous ***,a dual-polarized DRMED antenna is presented to construct a 2-element and 4×4 array for BS *** investigate its MIMO performance,sophisticated multi-cell scenario simulations are carried *** using the proposed dualpolarized DRMED array,the cellular system capacityis improved by 118.6%compared to a conventional DRA *** significant MIMO system improvement is mainly due to the reduced backward radiation and,therefore,reduced inter-cell *** align well with the simulations.

关键词： backward radiation dielectric resonator antennas(DRAs) isolation magnetoelectric-dipole(ME-dipole)antennas

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Scheduling Multiple Mobile Agents with Abilities of Sensing and Executing Simultaneously

Scheduling Multiple Mobile Agents with Abilities of Sensing ...

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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： Guo, Zhipeng Yu, Zhiyong Huang, Fangwan Tu, Chunyu Fuzhou University College of Computer and Data Science Fuzhou China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

ISBN: (纸本)9798350312270

How to schedule public resources to maximize the coverage of crowds has always been a hot topic. There are mainly two types of research. One is the scheduling of static resources, whose coverage efficiency is lower because the resources cannot be moved;the other is the dynamic scheduling of movable public resources, but the existing methods require additional devices to collect crowd volume data. To address this issue, this article introduces a new category of mobile agents that can sense and execute simultaneously. This means that an agent can simultaneously perform crowd volume data collection tasks and crowds coverage tasks in its area. Taking these multi-ability agents as public resources, this article proposes a scheduling method called GE-STMC (Greedy Execute - Spatial and Temporal Monte-Carlo dropout). The method considers the spatiotemporal correlation between areas and evaluates the sensing value and the executing value of areas comprehensively to allocate the agents accordingly. Through the experimental on the real data of Beijing Happy Valley, this method can get 16% larger crowds volume coverage than other baseline methods with the same number of agents. © 2023 IEEE.

关键词： Mobile agents

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