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2022 International Conference on Cloud computing, Internet of Things, and computer Applications, CICA 2022

作者： Zhang, Fawang Tang, Dan Cai, Ye College of Computer Science and Software Engineering Shenzhen University Shenzhen China State Key Laboratory of Computer Architecture Institute of Computing Technology Beijing China

ISBN: (纸本)9781510656673

In the field of high-performance computing, some application scenarios make extensive use of bit manipulation. RISC-V foundation issues B extension to reduce the number of instructions during the static compilation. But this extension does not cover the more frequent operations fully. Instruction fusion technology can fuse multiple instructions into one instruction at the decode stage to dynamically reduce the number of instructions. Therefore, this technology can supplement RISC-V B extension uncovered instruction, enhancing the processor’s capabilities for bit manipulation. This paper implements the RISC-V B extension 1.0 version on the Xiangshan processor. For accelerating more bit manipulation, we profiled general benchmarks by the simulator, proposed ten new instructions fusion targets, and implemented a fusion decode unit. Finally, we use Coremark and SPEC CPU 2006 integer to evaluate the performance. The Coremark results show that dynamic instructions reduce by 18.14%, and the performance improves by 12.09%. © 2022 SPIE.

关键词： Decoding

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Energy-Efficient and Reliable Federated Learning in Heterogeneous Mobile-Edge computing 19

Energy-Efficient and Reliable Federated Learning in Heteroge...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Xu, Yan Li, Liying Cong, Peijin Zhou, Junlong Nanjing University of Science and Technology School of Computer Science and Engineering China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Computer Architecture China

ISBN: (纸本)9798350346558

As a decentralized machine learning paradigm, Federated Learning (FL) is an emerging technique to protect user data privacy in Mobile Edge computing (MEC). FL adopts the idea of distributed privacy computing, enabling the terminal devices to train machine learning models required by servers locally and upload model parameters to servers for aggregation. However, in the process of coupling FL with MEC systems, device heterogeneity, malicious use of poisoned data by users and limited system resources will seriously affect FL training accuracy, system reliability and energy efficiency. In this paper, a highly reliable and energy-efficient FL scheme is designed to solve these issues. Specifically, we first propose a calculation method for estimating the reliability of heterogeneous devices. Then, we design a highly reliable device selection scheme based on devices' reliability, computing power, and participation times. Finally, we utilize the slack time in FL training to dynamically adjust the voltage and frequency of devices for reducing the energy consumption of training. Experimental results show that our proposed scheme improves the accuracy by up to 36.45% on average and saves energy by up to 48.63% when compared to the baseline and benchmarking methods. © 2022 IEEE.

关键词： Reliability

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Energy-Constrained Task Scheduling in Heterogeneous Distributed Systems 26

Energy-Constrained Task Scheduling in Heterogeneous Distribu...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Chen, Chen Zhu, Jie Huang, Haiping Gao, Yingmeng Nanjing University of Posts & Telecommunications Nanjing210003 China Institute of Computing Technology Academy of Sciences State Key Laboratory Chinese of Computer Architecture Beijing100864 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210003 China

ISBN: (纸本)9798350331684

The resource-constrained task scheduling problem has been one of the popular research topics in cloud computing systems. By employing the dynamic voltage and frequency scaling (DVFS) techniques, the task scheduling can be further constrained by energy consumption. The paper investigates the DAG task scheduling considering both the resource and energy constraints in heterogeneous distributed systems. The objective is to minimize the scheduling length. An energy-constrained task scheduling framework is employed, where tasks are initially scheduled according to their upward rank values. Then two heuristics are proposed to improve the initial solution, namely, the simulated annealing local search method and the frequency adjustment method. Experiments are conducted by testing a large number of instances with multiple parameter settings, and the results show that the proposed algorithms are effective and efficient. © 2023 IEEE.

关键词： Energy utilization

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Dynamic Task Allocation for Heterogeneous Multi-Robot System under Communication Constraints 6

Dynamic Task Allocation for Heterogeneous Multi-Robot System...

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

作者： Li, Jun Cai, Zhongxuan Li, Minglong Huang, Wanrong Zhang, Yongjun National University of Defense Technology Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer China

ISBN: (纸本)9781665460033

Multi-robot system has important application potential in disaster rescue and other dangerous scenarios, in which task allocation is the basis for multi-robot cooperation to complete tasks. Due to the limited ability of individual robots, many complex scenarios requires coordination of different types of robot, e.g. robots for search, communication, rescue and so on. At the same time, communication is often limited in a wide range of scenarios. This brings two challenges in the realistic environment: heterogeneous multi-robot cooperation problem and communication constraint problem. This paper proposes a task allocation method based on hierarchical auction and dynamic grouping. By introducing a dynamic grouping algorithm to the general allocation procedure, we simultaneously optimize the number of robots in each group and the communication distance between robots. Then the communication loss rate can be reduced to improve task execution efficiency. Simulations of a communication constrained search and rescue scenario are conducted to verify the proposed algorithm, which are compared with the vanilla auction algorithm and a hierarchical approach on static grouping. The results show that our method surpasses the baseline on both task achievement and communication performance. © 2023 IEEE.

关键词： Multipurpose robots

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LayerCFL:an efcient federated learning with layer-wised clustering

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Cybersecurity 2025年第1期8卷 72-85页

作者： Jie Yuan Rui Qian Tingting Yuan Mingliang Sun Jirui Li Xiaoyong Li School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijingChina Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of EducationBeijingChina Institute of Computer Science Faculty of Mathematics and Computer ScienceUniversity of GoettingenGöettingenGermany School of Information Technology Henan University of Chinese MedicineZhengzhouHenanChina

Federated Learning(FL)sufers from the Non-IID problem in practice,which poses a challenge for efcient and accurate model *** address this challenge,prior research has introduced clustered FL(CFL),which involves clustering clients and training them *** its potential benefts,CFL can be computationally and communicationally expensive when the data distribution is unknown *** is because CFL involves the entire neural networks of involved clients in computing the clusters during training,which can become increasingly timeconsuming with large-sized *** tackle this issue,this paper proposes an efcient CFL approach called LayerCFL that employs a Layer-wised clustering *** LayerCFL,clients are clustered based on a limited number of layers of neural networks that are pre-selected using statistical and experimental *** experimental results demonstrate the efectiveness of LayerCFL in mitigating the impact of Non-IID data,improving the accuracy of clustering,and enhancing computational efciency.

关键词： Federated learning Clustered federated learning Non-IID Layer-wised clustering

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PIOD: An Efficient Parallel Iterative Algorithm for Solving Over-determined Equations 29

PIOD: An Efficient Parallel Iterative Algorithm for Solving ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Tian, Min Wei, Zhenguo Li, Yanlong Du, Wei Xiao, Lei Xu, Chaoshuai China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks China Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Computing China

ISBN: (纸本)9798350330717

The solution of over-determined equations plays a very important role in fields such as data fitting, signal processing, and machine learning. It is of great significance in predicting natural phenomena, optimizing engineering design, and other fields. However, there is currently no efficient method to solve over-determined equations, either being not accurate enough or consuming a lot of time. In this article, we propose a parallel iterative method for solving over-determined equations, called the PIOD algorithm. By using a sub-convergence condition to terminate the iterative calculation, we have developed a task partitioning strategy for the algorithm and implemented parallelization of the solution of over-determined equations on a distributed memory system. Our proposed algorithm achieves an average speedup of 152 ×times compared to the open-source Eigen solver. Additionally, it also achieves a parallel efficiency of over 30%. © 2023 IEEE.

关键词： conjugate gradient method MPI over-determined equations parallel algorithms parallel iterative methods

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Automatic Target Description File Generation

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Journal of computer Science & technology 2023年第6期38卷 1339-1355页

作者：耿洪娜吕方钟茗崔慧敏薛景玲冯晓兵 State Key Laboratory of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Computer Science and Engineering University of New South WalesSydneyNSW 2052Australia

Agile hardware design is gaining increasing momentum and bringing new chips in larger quantities to the market ***,it also takes new challenges for compiler developers to retarget existing compilers to these new chips in shorter time than ever ***,retargeting a compiler backend,e.g.,an LLVM backend to a new target,requires compiler developers to write manually a set of target description files(totalling 10300+lines of code(LOC)for RISC-V in LLVM),which is error-prone and *** this paper,we introduce a new approach,Au-tomatic Target Description File Generation(ATG),which accelerates the generation of a compiler backend for a new tar-get by generating its target description files *** a new target,ATG proceeds in two ***,ATG synthesizes a small list of target-specific properties and a list of code-layout templates from the target description files of a set of existing targets with similar instruction set architectures(ISAs).Second,ATG requests compiler developers to fill in the information for each instruction in the new target in tabular form according to the list of target-specific properties syn-thesized and then generates its target description files automatically according to the list of code-layout templates *** first stage can often be reused by different new targets sharing similar *** evaluate ATG using nine RISC-V instruction sets drawn from a total of 1029 instructions in LLVM *** enables compiler developers to gen-erate compiler backends for these ISAs that emit the same assembly code as the existing compiler backends for RISC-V but with significantly less development effort(by specifying each instruction in terms of up to 61 target-specific properties only).

关键词： retargetability compiler target description target backend automatic generator

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HTDet:A Clustering Method Using Information Entropy for Hardware Trojan Detection

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Tsinghua Science and technology 2021年第1期26卷 48-61页

作者： Renjie Lu Haihua Shen Zhihua Feng Huawei Li Wei Zhao Xiaowei Li the University of Chinese Academy of Sciences Beijing 101408China Beijing Institute of Computer Technology and Application Beijing 100854China the State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China

Hardware Trojans(HTs)have drawn increasing attention in both academia and industry because of their significant potential *** this paper,we propose HTDet,a novel HT detection method using information entropybased *** maintain high concealment,HTs are usually inserted in the regions with low controllability and low observability,which will result in that Trojan logics have extremely low transitions during the *** implies that the regions with the low transitions will provide much more abundant and more important information for HT *** HTDet applies information theory technology and a density-based clustering algorithm called Density-Based Spatial Clustering of Applications with Noise(DBSCAN)to detect all suspicious Trojan logics in the circuit under *** DBSCAN is an unsupervised learning algorithm,that can improve the applicability of *** addition,we develop a heuristic test pattern generation method using mutual information to increase the transitions of suspicious Trojan *** on circuit benchmarks demonstrate the effectiveness of HTDet.

关键词： Hardware Trojan(HT)detection information entropy Density-Based Spatial Clustering of Applications with Noise(DBSCAN) unsupervised learning clustering mutual information test patterns generation

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Self-distillation Enhanced Vertical Wavelet Spatial Attention for Person Re-identification 1

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Zhang, Yuxuan Tan, Huibin Lan, Long Teng, Xiao Ren, Jing Zhang, Yongjun Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Beijing100071 China

ISBN: (数字)9783031533082

ISBN: (纸本)9783031533075

Person re-identification is a challenging problem in computer vision, aiming to accurately match and recognize the same individual across different viewpoints and cameras. Due to significant variations in appearance under different scenes, person re-identification requires highly discriminative features. Wavelet features contain richer phase and amplitude information as well as rotational invariance, demonstrating good performance in various visual tasks. However, through our observations and validations, we have found that the vertical component within wavelet features exhibits stronger adaptability and discriminability in person re-identification. It better captures the body contour and detailed information of pedestrians, which is particularly helpful in distinguishing differences among individuals. Based on this observation, we propose a vertical wavelet spatial attention only with the vertical component in the high frequency specifically designed for feature extraction and matching in person re-identification. To enhance spatial semantic consistency and facilitate the transfer of knowledge between different layers of wavelet attention in the neural network, we introduce a self-distillation enhancement method to constrain shallow and deep spatial attention. Experimental results on Market-1501 and DukeMTMC-reID datasets validate the effectiveness of our model. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Distillation

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IntNet: A novel framework using reconstructed mutation samples and a biologically informed neural network for pathway analysis

IntNet: A novel framework using reconstructed mutation sampl...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhou, Li Li, Jie Tan, Weilong Harbin Institute of Technology School of Computer Science and Technology Harbin China National Key Laboratory of Smart Farming Technology and Systems College of Computing Harbin Institute of Technology Harbin China Technology Center for Medicines Center for Medicines Nanjing China

ISBN: (纸本)9798350386226

Most pathway analysis methods based on simple nucleotide variations overlook mutations occurring in genes outside the pathway, fail to address mutation heterogeneity with targeted measures, and do not consider the functional relationships among gene components within biomolecular networks when modeling the contributions of biomarkers to pathways. So, we proposed a novel framework, named IntNet, for pathway analysis based on simple nucleotide variations. The core components of IntNet are sample reconstruction (SC) and a new biologically informed neural network model (BINN). To address the difficulty of extracting effective features caused by the mutation heterogeneity, IntNet reconstructs mutation samples based on the hypothesis that mutations in patients with the same phenotype may cluster in similar network regions. BINN uses the human gene network and expanded biological pathways to construct its neural network architecture. BINN implements mutation signaling propagation along the human gene network and integration within the pathways. IntNet's performance was validated using multiple cancer datasets. Experimental results demonstrated that IntNet could identify more accurate and reliable pathways. © 2024 IEEE.

关键词： Nucleotides

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