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Eco-FL: Adaptive Federated Learning with Efficient Edge Collaborative Pipeline Training 22

Eco-FL: Adaptive Federated Learning with Efficient Edge Coll...

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Proceedings of the 51st International conference on parallel processing

作者： Shengyuan Ye Liekang Zeng Qiong Wu Ke Luo Qingze Fang Xu Chen Sun Yat-sen University China

ISBN: (纸本)9781450397339

Federated Learning (FL) has been a promising paradigm in distributed machine learning that enables in-situ model training and global model aggregation. While it can well preserve private data for end users, to apply it efficiently on IoT devices yet suffer from their inherent variants: their available computing resources are typically constrained, heterogeneous, and changing dynamically. Existing works deploy FL on IoT devices by pruning a sparse model or adopting a tiny counterpart, which alleviates the workload but may have negative impacts on model accuracy. To address these issues, we propose Eco-FL, a novel Edge Collaborative pipeline based Federated Learning framework. On the client side, each IoT device collaborates with trusted available devices in proximity to perform pipeline training, enabling local training acceleration with efficient augmented resource orchestration. On the server side, Eco-FL adopts a novel grouping-based hierarchical architecture that combines synchronous intra-group aggregation and asynchronous inter-group aggregation, where a heterogeneity-aware dynamic grouping strategy that jointly considers response latency and data distribution is developed. To tackle the resource fluctuation during the runtime, Eco-FL further applies an adaptive scheduling policy to judiciously adjust workload allocation and client grouping at different levels. Extensive experimental results using both prototype and simulation show that, compared to state-of-the-art methods, Eco-FL can upgrade the training accuracy by up to 26.3%, reduce the local training time by up to 61.5%, and improve the local training throughput by up to 2.6 ×.

关键词： edge intelligence pipeline parallelism Federated learning parallel processing

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Optimal Subgraph Matching Queries over distributed Knowledge Graphs Based on Partial Evaluation 22nd

Optimal Subgraph Matching Queries over Distributed Knowledge...

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22nd International conference on Web Information Systems Engineering (WISE)

作者： Xing, Jiao Liu, Baozhu Li, Jianxin Choudhury, Farhana Murtaza Wang, Xin Tianjin Univ Tianjin Int Engn Inst Tianjin Peoples R China Deakin Univ Sch Informat Technol Melbourne Australia Univ Melbourne Sch Comp & Informat Syst Melbourne Australia

ISBN: (纸本)9783030908881;9783030908874

The partial evaluation and assembly framework has recently been applied for processing subgraph matching queries over large-scale knowledge graphs in the distributed environment. The framework is implemented on the master-slave architecture, endowed with outstanding scalability. However, there exists a drawback of partial evaluation, that if the volume of intermediate results is large, assembly computation which is handled by the master would be a bottleneck. In this paper, we propose an efficient assembly algorithm by exploring the characteristics of intermediate results generated during the partial matching stage in each fragment. (1) The partial matching index (PM-Index) structure are constructed by utilizing the incoming and outgoing vertices in parallel to improve the searching efficiency of the assembling phase. (2) The time and space complexity of PM-Index construction are analysed. (3) The experimental results over benchmark datasets show that our approach outperforms the state-of-the-art methods.

关键词： Partial evaluation Subgraph matching RDF graph

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A Dynamic Mapping Model for General CNN Accelerator Based on FPGA 17th

A Dynamic Mapping Model for General CNN Accelerator Based on...

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17th IFIP WG 10.3 International conference on Network and parallel Computing, NPC 2020

作者： Zhao, Xiaoqiang Jiang, Jingfei Han, Zhe Xu, Jinwei Liu, Zhiqiang National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Artificial Intelligence Research Center National Innovation Institute of Defense Technology Beijing China

ISBN: (纸本)9783030794774

As the application scenarios of convolutional neural network (CNN) become more and more complex, the general CNN accelerator based on matrix multiplication has become a new research focus. The existing mapping methods for converting convolution calculation into matrix multiplication need to be improved. This paper proposes a new dynamic mapping model to improve the flexibility and versatility of matrix multiplication. The dynamic mapping model implements two algorithms: dynamic residue processing mapping algorithm (DRPMA) and dilated convolution mapping algorithm (DCMA). The former can dynamically adjust the mapping method according to the number of output channels of the convolution layer, improve the utilization of the multiply-accumulate (MAC) array. The latter extends the efficient support for Dilated CNNs. For demonstration, we implement an accelerator with Verilog on Xilinx VC709 FPGA board and test some typical CNN models. Experimental results show that the general accelerator achieves high performance and energy efficiency. © 2021, IFIP International Federation for Information processing.

关键词： Field programmable gate arrays (FPGA)

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Exploring Policy Diversity in parallel Actor-Critic Learning

Exploring Policy Diversity in Parallel Actor-Critic Learning

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International conference on Tools for Artificial Intelligence (ICTAI)

作者： Yanqiang Zhang Yuanzhao Zhai Gongqian Zhou Bo Ding Dawei Feng Songwang Liu National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Academy of Military Science Beijing China

Exploration is a critical challenge for deep reinforcement learning methods. Although existing works such as actor-critic algorithms have made much progress, most still suffer from the sample inefficiency problem in complex environments where rewards are sparse. parallel sampling, which uses multiple actors with the same policy interacting with the environment, is an effective approach to improve sample efficiency. However, parallel parameter-sharing actors collect similar samples, which generally hinders the improvement of the overall exploration process. In this paper, we propose a Policy Diversity enhanced approach for parallel Actor-Critic (PDAC). Specifically, we extend the parallel actor-critic architecture to the PDAC framework composed of a shared critic and parallel distinct actors. Then we introduce the KL-divergence of the action probability distribution between parallel actors as the intrinsic reward to encourage actors to explore diverse strategies. We evaluate our approach in multiple challenging procedurally-generated tasks and compare it with state-of-the-art algorithms. Experiments show that PDAC makes significant progress in the comparison, in terms of cumulative rewards and sample efficiency.

关键词： Deep learning Reinforcement learning Learning (artificial intelligence) Probability distribution Task analysis

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Introducing a Stream processing Framework for Assessing parallel Programming Interfaces

Introducing a Stream Processing Framework for Assessing Para...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Adriano Marques Garcia Dalvan Griebler Luiz G. L. Fernandes Claudio Schepke School of Technology Pontifical Catholic University of Rio Grande do Sul (PUCRS) Porto Alegre Brazil Laboratory of Advanced Research on Cloud Computing (LARCC) Três de Maio Maio Faculty (SETREM) Três de Maio Brazil Federal University of Pampa (UNIPAMPA) Alegrete Brazil

Stream processing applications are spread across different sectors of industry and people's daily lives. The increasing data we produce, such as audio, video, image, and text are demanding quickly and efficiently computation. It can be done through Stream parallelism, which is still a challenging task and most reserved for experts. We introduce a Stream processing framework for assessing parallel Programming Interfaces (PPIs). Our framework targets multi-core architectures and C++ stream processing applications, providing an API that abstracts the details of the stream operators of these applications. Therefore, users can easily identify all the basic operators and implement parallelism through different PPIs. In this paper, we present the proposed framework, implement three applications using its API, and show how it works, by using it to parallelize and evaluate the applications with the PPIs Intel TBB, FastFlow, and SPar. The performance results were consistent with the literature.

关键词： Industries parallel programming distributed databases Computer architecture Streaming media parallel processing Task analysis

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An efficient and scalable SPARK preprocessing methodology for Genome Wide Association Studies 28

An efficient and scalable SPARK preprocessing methodology fo...

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28th Euromicro International conference on parallel, distributed and Network-Based processing (PDP)

作者： Agapito, Giuseppe Guzzi, Pietro Hiram Cannataro, Mario Univ Magna Grcecia Catanzaro Dept Med & Surg Sci Data Analyt Res Ctr Catanzaro Italy

ISBN: (纸本)9781728165820

The importance of the use of high-performance software frameworks to analyze omics data obtained by using High-Throughput (HT) essays is widely recognized. HT methodologies comprise microarrays, Genome-Wide Association Studies (GWAS), and Next Generation Sequencing (NGS), which provide a vast amount of data per a single experiment. Each HT vendor provides to the users only the software frameworks and the proprietary libraries for the annotation, and summarization of raw data. Consequently, the needs of algorithms for the preprocessing and analysis of omics data arise. GWAS aims to highlight the association between genetic variants and diseases by examining single nucleotide polymorphisms (SNPs), which differ in a statistically significant way between cases and controls. The effectiveness of GWAS analysis increases with the number of analyzed samples per single experiment. GWAS data analyzed through the use of statistical methods can detect associations among a single allelic variant and the clinical conditions of samples. To overcome these limitations, and to make it possible to discover multiple associations among allelic variants, it is possible to use Association Rules mining. Consequently, the need for the introduction of scalable Association Rule Mining (ARM) algorithms able to analyze GWAS data arises. Hence, the use of high-performance data analytics framework is needed. For this purpose, we propose a software framework called GARMS (GWAS Association Rule Mining in Spark) built on top of Apache Spark for the preprocessing, and mining of association rules from GWAS data sets. GARMS comprises a two steps analysis methodology: (i) in the first step, the GWAS data are preprocessed, along with the identification of the frequent itemsets;(ii) in the second step, frequent itemsets are employed to mine association rules without scanning the input data. We implemented our algorithm, and we tested it on some synthetic GWAS data sets. Preliminary results confirm that ou

关键词： Apache Spark distributed Computing GWAS SNPs Preprocessing Association Rules

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A New Statistical Iterative Reconstruction Algorithm for a CT Scanner with Flying Focal Spot 20th

A New Statistical Iterative Reconstruction Algorithm for a C...

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20th International conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Cierniak, Robert Pluta, Piotr Czestochowa Tech Univ Dept Intelligent Comp Syst Armii Krajowej 36 PL-42200 Czestochowa Poland

ISBN: (纸本)9783030878979;9783030878962

This work is related to the originally formulated 3D statistical model-based iterative reconstruction algorithm adopted to computed tomography with flying focal spot. This new reconstruction method is based on a continuous-to-continuous data model, where the forward model is formulated as a shift invariant system. The proposed approach resembles the well-known Feldkamp (FDK) algorithm, which cannot be used with a flying focal spot scanner as the paths of the X-rays used are not equi-angularly distributed. In this situation, a so-called "nutating" reconstruction algorithm is usually used, which is based on a rebinning methodology, thus transforming the reconstruction problem to that of a parallel beam system. Our approach has some significant advantages compared with the FBP methods. Moreover, although our method belongs to the category of iterative reconstruction approaches, thanks to the fact that our proposed model is derived as a shift invariant system, it is possible to use an FFT algorithm to accelerate the calculations that have to be performed. Because of this, we can obtain diagnostic images in a time comparable to that of FBP methods. Computer simulations have shown that the reconstruction method presented here outperforms referential FBP methods with regard to the image quality obtained and can be competitive in terms of time of calculation.

关键词： image reconstruction from projections X-ray computed tomography Statistical reconstruction algorithm Flying focal spot

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The research of implementation method of canny edge detection of video on FPGA

The research of implementation method of canny edge detectio...

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2020 International conference on Engineering Management of Communication and Technology, EMCTECH 2020

作者： Tan, Li Quan He, Bin School of Information Science and Technology Beijing University of Chemical Technology 100029 China

ISBN: (纸本)9781665404488

Edge detection algorithm has an important application in video image processing. The algorithm overcomes the shortcomings of traditional video image edge detection methods, and significantly improves the quality of video image edge detection. However, the amount of calculation is very large, and the traditional implementation method is difficult to meet the real-time requirements of video image edge detection, and the implementation cost is high. In this design, field programmable gate array (FPGA) is used as the core to construct and implement Canny video image edge detection system. Firstly, the video image is collected by ov7725 camera in real time;then, the edge detection of video image based on Canny algorithm is implemented in FPGA. Finally, the effect of Canny video image edge detection is compared and analyzed. This design has a good reference value for canny video image edge detection. © 2020 IEEE.

关键词： image enhancement

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11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge Workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st Workshop/Special Session on Machine Learning and Big Data in Health Care, ML@HC 2022 and 2nd Workshop on Artificial Intelligence in Biomedical Engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 International conference on Artificial Intelligence Applications and Innovations, AIAI 2022

11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putt...

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11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge Workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st Workshop/Special Session on Machine Learning and Big Data in Health Care, ML@HC 2022 and 2nd Workshop on Artificial Intelligence in Biomedical Engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 International conference on Artificial Intelligence Applications and Innovations, AIAI 2022

ISBN: (纸本)9783031083402

The proceedings contain 35 papers. The special focus in this conference is on Mining Humanistic Data. The topics include: Digitally Assisted Planning and Monitoring of Supportive Recommendations in Cancer Patients;CAIPI in Practice: Towards Explainable Interactive Medical image Classification;a Deep Q Network-Based Multi-connectivity Algorithm for Heterogeneous 4G/5G Cellular Systems;simulating Blockchain Consensus Protocols in Julia: Proof of Work vs Proof of Stake;Maximum Likelihood Estimators on MCMC Sampling Algorithms for Decision Making;employing Natural Language processing Techniques for Online Job Vacancies Classification;Probabilistic Quantile Multi-step Forecasting of Energy Market Prices: A UK Case Study;proactive Buildings: A Prescriptive Maintenance Approach;performance Meta-analysis for Big-Data Univariate Auto-Imputation in the Building Sector;non-intrusive Diagnostics for Legacy Heat-Pump Performance Degradation;a 5G-Based Architecture for Localization Accuracy;anomaly Detection in Small-Scale Industrial and Household Appliances;an Innovative Software Platform for Efficient Energy, Environmental and Cost Planning in Buildings Retrofitting;deep Learning-Based Segmentation of the Atherosclerotic Carotid Plaque in Ultrasonic images;An Intelligent Grammar-Based Platform for RNA H-type Pseudoknot Prediction;An Automated 2D U-Net Segmentation Method for the Identification of Cancer Brain Metastases Using MRI images;The Use of Robotics in Critical Use Cases: The 5G-ERA Project Solution;fundamental Features of the Smart5Grid Platform Towards Realizing 5G Implementation;experimentation Scenarios for Machine Learning-Based Resource Management;efficient Data Management and Interoperability Middleware in Business-Oriented Smart Port Use Cases;5G for the Support of Smart Power Grids: Millisecond Level Precise distributed Generation Monitoring and Real-Time Wide Area Monitoring;monitoring Neurological Disorder Patients via Deep Learning Based Facial Expressions An

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Using Programmable P4 Switches to Reduce Communication Costs of parallel and distributed Simulations

Using Programmable P4 Switches to Reduce Communication Costs...

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GLOBECOM 2022 - 2022 IEEE Global Communications conference

作者： Shie–Yuan Wang Nai–En Kuo Department of Computer Science National Yang Ming Chiao Tung University Taiwan

ISBN: (纸本)9781665435413

In the past, several time synchronization methods have been proposed for parallel and distributed simulation (PDS). Among them, one widely used conservative method is the Chandy-Misra-Bryant (CMB) algorithm. In the CMB algorithm, many null messages may be exchanged among logical processes to advance their clocks so that deadlock will not occur among them. In this work, using a data-plane programmable P4 hardware switch, we design and implement a data fusion-based approach inside the packet processing pipeline of the P4 switch. Our approach extracts the timestamp carried in exchanged null messages, computes the fusion results of these timestamps, drops unnecessary null messages inside the switch, generates new messages carrying the fusion results, and sends these generated messages to only the logical processes that can benefit from receiving these messages. Experimental results show that on an 8-host testbed, our approach can speed up a PDS by a factor of 2.75 and 1.65 when compared with the unicast and multicast approaches, respectively.

关键词： Costs Multicast algorithms Unicast Pipelines Switches System recovery Hardware

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