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COPPER:a combinatorial optimization problem solver with processing-in-memory architecture

Frontiers of Information Technology & Electronic engineering 2023年第5期24卷 731-741页

作者： Qiankun WANG Xingchen LI Bingzhe WU Ke YANG Wei HU Guangyu SUN Yuchao YANG School of Software&Microelectronics Peking UniversityBeijing 100871China School of Computer Science Peking UniversityBeijing 100871China School of Integrated Circuits Peking UniversityBeijing 100871China Tencent AI Lab Shenzhen 518057China College of Physics and Information Engineering Fuzhou UniversityFuzhou 350116China Beijing Advanced Innovation Center for Integrated Circuits Beijing 100871China Beijing Academy of Artificial Intelligence Beijing 100080China

The combinatorial optimization problem(COP),which aims to find the optimal solution in discrete space,is fundamental in various ***,many COPs are NP-complete,and require much more time to solve as the problem scale *** by this,researchers may prefer fast methods even if they are not exact,so approximation algorithms,heuristic algorithms,and machine learning have been *** works proposed chaotic simulated annealing(CSA)based on the Hopfield neural network and did a good ***,CSA is not something that current general-purpose processors can handle easily,and there is no special hardware for *** efficiently perform CSA,we propose a software and hardware *** software,we quantize the weight and output using appropriate bit widths,and then modify the calculations that are not suitable for hardware *** hardware,we design a specialized processing-in-memory hardware architecture named COPPER based on the *** is capable of efficiently running the modified quantized CSA algorithm and supporting the pipeline further *** results show that COPPER can perform CSA remarkably well in both speed and energy.

关键词： Combinatorial optimization Chaotic simulated annealing Processing-in-memory

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3DS: An Efficient DPDK-based Data Distribution Service for Distributed Real-time Applications

3DS: An Efficient DPDK-based Data Distribution Service for D...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Tianyu Xu Xianzhang Chen Changze Wu Jiapin Wang Rongwei Zheng Duo Liu Yujuan Tan Ao Ren Jian Li College of Computer Science Chongqing University Chongqing China School of Big Data and Software Engineering Chongqing University Chongqing China School of Electrical Engineering Chongqing University Chongqing China

Data Distribution Service (DDS) is a widely-used middleware for data transmission in distributed real-time applications, such as autonomous vehicles and robotics. However, the communication of existing DDS middlewares generally relies on the network stacks of the operating system, which brings long latency to the real-time applications. In this paper, we propose a DPDK-based DDS, dubbed 3DS, to improve the performance of communication of DDS middlewares. First, 3DS reduces the latency of communication between distributed real-time applications by avoiding the latency of kernel. Second, 3DS reduces the overhead of dynamic memory allocation and deallocation by a reserved memory pool. Finally, 3DS adopts a thread-exclusive CPU model to reduce the resource contention between threads. We implement and evaluate 3DS on real systems running Linux. The experimental results show that 3DS reduces 44% of the communication latency over FastDDS, which is the state-of-the-art DDS middleware used by ROS2. Moreover, the throughput of data transfer in 3DS can be 1.51 times that of FastDDS.

关键词： Three-dimensional displays Smart cities Instruction sets Distributed databases Throughput Real-time systems Virtual machining

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Weak Network Oriented Mobile Distributed Storage: A Hybrid Fault-Tolerance Scheme Based on Potential Replicas

Weak Network Oriented Mobile Distributed Storage: A Hybrid F...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Xinlei Wei Yujuan Tan Duo Liu Daitao Wu Yu Wu Xianzhang Chen Jian Li College of Computer Science Chongqing University Chongqing China School of Big Data & Software Engineering Chongqing University Chongqing China School of Electrical Engineering Chongqing University Chongqing China

Node failure is one of the most typical issues in distributed storage systems. The classic fault-tolerance methods can meet the fault tolerance needs of systems deployed in edge storage, 5G IoT, and other high-performance data centers. However, mobile distributed systems contend with inconsistent network signals and relatively low bandwidth in weak network environments. The traditional fault-tolerance schemes have difficulty ensuring distributed storage and data repair simultaneously, posing a considerable challenge to data storage reliability. To address this problem, we proposed a hybrid fault-tolerance scheme based on potential replicas, called HFPR, which trades off storage overhead and performance for weak network mobile distributed storage systems. HFPR first stores data with the erasure code redundancy and then gradually increases data redundancy by reserving potential replicas, enabling the system to disperse network transmission pressure and reduce network transmission. To maximize replica utilization and control the storage overhead of potential replicas, we design a node state prediction mechanism and a file lifecycle management mechanism. Evaluation results show that, compared with the traditional hybrid fault-tolerant scheme with the same storage overhead, HFPR achieves better replica-level repair efficiency in data repair and a reduced probability of degraded reads by approximately 50%. During data distribution, HFPR can minimize data write amplification by up to 1.1× and additional network transfers by up to 77.5%.

关键词： Data centers Fault tolerant systems Redundancy Distributed databases Memory Bandwidth Maintenance engineering

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Identity-checking for provable data possession without privacy leakage 6th

Identity-checking for provable data possession without priva...

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6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Wang, Jiaqi Zeng, Shengke Li, Shiliang School of Computer and Software Engineering Xihua University Chengdu610039 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9789811581007

The user data stored in remote server should be assured intact. Remote data integrity checking scheme (RDIC) allows a third party auditor (TPA) to examine the integrity of data in cloud server. As we know, the user authentication is necessary in the cloud storage. Obviously, the user identity is sensitive as the user can be identified when it requests integrity checking. In this paper, we employ zero-knowledge undeniable signature to RDIC to tackle the privacy leakage. There is no evidence to show the user checking, thus the user privacy is protected. © Springer Nature Singapore Pte Ltd 2020.

关键词： Cloud storage

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Quality of Service (QoS)-driven Edge Computing and Smart Hospitals: A Vision, Architectural Elements, and Future Directions

arXiv

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arXiv 2023年

作者： Buyya, Rajkumar Srirama, Satish N. Mahmud, Redowan Goudarzi, Mohammad Ismail, Leila Kostakos, Vassilis Lab School of Computing and Information Systems The University of Melbourne Australia School of Computer and Information Sciences The University of Hyderabad India School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia Lab Department of Computer Science and Software Engineering National Water and Energy Center United Arab Emirates University United Arab Emirates

The Internet of Things (IoT) paradigm is drastically changing our world by making everyday objects an integral part of the Internet. This transformation is increasingly being adopted in the healthcare sector, where Smart Hospitals are now relying on IoT technologies to track staff, patients, devices, and equipment, both within a hospital and beyond. This paradigm opens the door to new innovations for creating novel types of interactions among objects, services, and people in smarter ways to enhance the quality of patient services and the efficient utilisation of resources. However, the realisation of real-time IoT applications in healthcare and, ultimately, the development of Smart Hospitals are constrained by their current Cloud-based computing environment. Edge computing emerged as a new computing model that harnesses edge-based resources alongside Clouds for real-time IoT applications. It helps to capitalise on the potential economic impact of the IoT paradigm of $11 trillion per year, with a trillion IoT devices deployed by 2025 to sense, manage and monitor the hospital systems in real-time. This vision paper proposes new algorithms and software systems to tackle important challenges in Edge computing-enabled Smart Hospitals, including how to manage and execute diverse real-time IoT applications and how to meet their diverse and strict Quality of Service (QoS) requirements in hospital settings. The vision we outline can help tackle timely challenges that hospitals increasingly face. © 2023, CC BY.

关键词： Internet of things

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An Application of SVD++ Method in Collaborative Filtering 17

An Application of SVD++ Method in Collaborative Filtering

引用

17th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2020

作者： Jiang, Sheng Li, Jianping Wang, Zhou School of Computer Science and Engineering University of Electronic Science and Technology of China Chcnadu610054 China School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9781665405058

Collaborative filtering algorithms have important applications in the implementation of recommendation systems. Collaborative filtering generally focuses on the user's evaluation scores for commodity items, and these evaluation data imply some specific relevance. Therefore, this type of collaborative filtering Model research has attracted widespread attention. SVD is currently one of the most classic and practical collaborative filtering algorithm models. It can deal with the problem of data sparseness in the recommendation system and derive different types of SVD models on this basis, but its recommendation results It has never been significantly improved. In response to such problems, this article proposes a new variant of the SVD++ algorithm that incorporates a special timing mechanism for dynamic adjustment, and uses the average absolute error, root mean square error and standard average absolute error for the recommended results. After evaluation, we got better results than the current classic SVD++ model. © 2020 IEEE.

关键词： Singular value decomposition

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MEA-Defender: A Robust Watermark against Model Extraction Attack

arXiv

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arXiv 2024年

作者： Lv, Peizhuo Ma, Hualong Chen, Kai Zhou, Jiachen Zhang, Shengzhi Liang, Ruigang Zhu, Shenchen Li, Pan Zhang, Yingjun Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China Department of Computer Science Metropolitan College Boston University United States Institute of Software Chinese Academy of Sciences China

Recently, numerous highly-valuable Deep Neural Networks (DNNs) have been trained using deep learning algorithms. To protect the Intellectual Property (IP) of the original owners over such DNN models, backdoor-based watermarks have been extensively studied. However, most of such watermarks fail upon model extraction attack, which utilizes input samples to query the target model and obtains the corresponding outputs, thus training a substitute model using such input-output pairs. In this paper, we propose a novel watermark to protect IP of DNN models against model extraction, named MEA-Defender. In particular, we obtain the watermark by combining two samples from two source classes in the input domain and design a watermark loss function that makes the output domain of the watermark within that of the main task samples. Since both the input domain and the output domain of our watermark are indispensable parts of those of the main task samples, the watermark will be extracted into the stolen model along with the main task during model extraction. We conduct extensive experiments on four model extraction attacks, using five datasets and six models trained based on supervised learning and self-supervised learning algorithms. The experimental results demonstrate that MEA-Defender is highly robust against different model extraction attacks, and various watermark removal/detection approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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A Vision-Based Algorithm of Slope Angle Estimation by Using Cooperation of Snake Robots

A Vision-Based Algorithm of Slope Angle Estimation by Using ...

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IEEE International Conference on Mechatronics and Automation

作者： Shuai Li Jian Liu Wenjun Liu Ziming Liu Jingrun Tian Ruonan Wang Xuan Xiao Zengzeng Wang School of Software Tiangong University Tianjin China Center of Engineering Practice Training Tiangong University Tianjin China School of Economics&Management Tiangong University Tianjin China School of Computer Science&Technology Tiangong University Tianjin China

When snake robots perform tasks, their ability of sensing the environment is very important. This paper proposes an algorithm for estimating the slope angle based on the cooperation of two snake robots by using the visual system. Through the movement of two snake robots, the corresponding geometric models will be created. After that, the angle of the slope is calculated by substituting the coordinates that are recorded by the visual system. Finally, the experiments are conducted to verify the performance of the algorithm. The results show the errors are within 2° when the slope range is between 15° and 75°.

关键词：

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Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information

Unsupervised Semantic Representation Learning of Scientific ...

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IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Hongrui Gao Yawen Li Meiyu Liang Zeli Guan Beijing Key Laboratory of Intelligent Communication Software and Multimedia School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing School of Economics and Management Beijing University of Posts and Telecommunications Beijing

ISBN: (纸本)9781665456579

Since most scientific literature data are unlabeled, this makes unsupervised graph-based semantic representation learning crucial. Therefore, an unsupervised semantic representation learning method of scientific literature based on graph attention mechanism and maximum mutual information (GAMMI) is proposed. By introducing a graph attention mechanism, the weighted summation of nearby node features makes the weights of adjacent node features entirely depend on the node features. Depending on the features of the nearby nodes, different weights can be applied to each node in the graph. In addition, an unsupervised graph contrastive learning strategy is proposed to solve the problem of being unlabeled and scalable on large-scale graphs. By comparing the mutual information between the positive and negative local node representations on the latent space and the global graph representation, the graph neural network can capture both local and global information. Experimental results demonstrate competitive performance on various node classification benchmarks, achieving good results and sometimes even surpassing the performance of supervised learning.

关键词： Representation learning Cloud computing Semantics Supervised learning computer architecture Benchmark testing Graph neural networks

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TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities

arXiv

引用

arXiv 2022年

作者： Zhao, Zhe Li, Yudong Hou, Cheng Zhao, Jing Tian, Rong Liu, Weijie Chen, Yiren Sun, Ningyuan Liu, Haoyan Mao, Weiquan Guo, Han Guo, Weigang Wu, Taiqiang Zhu, Tao Shi, Wenhang Chen, Chen Huang, Shan Chen, Sihong Liu, Liqun Li, Feifei Chen, Xiaoshuai Sun, Xingwu Kang, Zhanhui Du, Xiaoyong Shen, Linlin Yan, Kimmo Tencent AI Lab China School of Computer Science and Software Engineering Shenzhen University China School of Information and DEKE MOE Renmin University of China China

Recently, the success of pre-training in text domain has been fully extended to vision, audio, and cross-modal scenarios. The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework. In this paper, we present TencentPretrain, a toolkit supporting pre-training models of different modalities. The core feature of TencentPretrain is the modular design. The toolkit uniformly divides pre-training models into 5 components: embedding, encoder, target embedding, decoder, and target. As almost all of common modules are provided in each component, users can choose the desired modules from different components to build a complete pre-training model. The modular design enables users to efficiently reproduce existing pre-training models or build brand-new one. We test the toolkit on text, vision, and audio benchmarks and show that it can match the performance of the original implementations. © 2022, CC BY.

关键词： Embeddings

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