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检索条件"任意字段=International Symposium on Software Engineering for Parallel and Distributed Systems"

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FPGA Accelerated Convolutional Neural Network for Detection of Cardiac Arrhythmia 4

FPGA Accelerated Convolutional Neural Network for Detection ...

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4th IEEE international Conference on VLSI systems, Architecture, Technology and Applications, VLSI SATA 2024

作者： Mangaraj, Soumyashree Oraon, Pawan Ari, Samit Swain, Ayas Kanta Mahapatra, Kamalakanta National Institute Of Technology Department Of Electronics And Communication Engineering Rourkela India

ISBN: (纸本)9798350362268

Electrocardiogram signals (ECG) are vital features to identify a healthy body;diagnosing cardiovascular diseases (CVDs) automatically with resource constraint devices at the edge has caught a significant attention in the rapid growth of smart health-care system. The rapidly developing smart world of IoT demands for low latency, less complex, and energy efficient architectures, and their deployment in real time scenarios. Field Programmable Gate Arrays (FPGAs) is a trading distributed embedded platform proving parallel processing, and flexibility in re-design with its re-configurable advancements. This work demonstrates a spilt 2D convolutional neural network (CNN) performing ECG beat classification on Xilinx's ZCU 104 FPGA board. The convolution has been carried out depth-wise, and performing a channel-wise addition of the half-split feature maps, so as to minimize the resource utilization by reducing the trainable parameters. A customized FPGA IP has been generated using Vitis High Level Synthesis (HLS) tool, and is implemented on ZCU 104 FPGA for the proposed less complex light-weight model. The design has achieved an accuracy of 98.64% in software, and consumed a total power of 4.177W with a latency of 2.19E8ns in classifying arrhythmia reference to Association for the Advancement of Medical Instrumentation-AAMI EC57 standard. © 2024 IEEE.

关键词： Electrocardiograms

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CachedArrays: Optimizing Data Movement for Heterogeneous Memory systems

CachedArrays: Optimizing Data Movement for Heterogeneous Mem...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Mark Hildebrand Jason Lowe-Power Venkatesh Akella University of California Davis

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

We propose a new framework called CachedArrays and a set of APIs to address the data tiering problem in large scale heterogeneous and disaggregated memory systems. The proposed framework operates at a variable size object granularity and allows the programmer to specify semantic hints about future use of data via a Policy API, which are used by a Data Manager to choose when and where to place a particular data object using a data management API, thus bridging the semantic gap between the programmer and the platform-specific hardware details, and optimizing overall performance. We evaluate the proposed framework on a real hardware platform with terabytes of memory consisting of NVRAM and DRAM on large scale ML training workloads such CNNs that exhibit different data access and usage patterns. We show that CachedArrays outperforms hardware caches, and can exploit many of the algorithmic-specific optimizations of prior works.

关键词： Training distributed processing Runtime Nonvolatile memory Semantics software algorithms Random access memory

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Multiplicative Weights Algorithms for parallel Automated software Repair 35

Multiplicative Weights Algorithms for Parallel Automated Sof...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Renzullo, Joseph Weimer, Westley Forrest, Stephanie Arizona State Univ Tempe AZ 85281 USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781665440660

Multiplicative Weights Update (MWU) algorithms are a form of online learning that is applied to multi-armed bandit problems. Such problems involve allocating a fixed number of trials among multiple options to maximize cumulative payoff. MWU is a popular and effective method for dynamically balancing the trade-off between exploring the value of new options and exploiting the information already gained. However, no clear strategy exists to help practitioners choose which of the several algorithmic designs within this family to deploy. In this paper, three variants of parallel MWU algorithms are considered: Two parallel variants that rely on global memory, and one variant that uses distributed memory. The three variants are first analyzed theoretically, and then their effectiveness is assessed empirically on the task of estimating distributions in the context of stochastic search for repairs to bugs in software. Earlier work on APR suffers from various inefficiencies, and the paper shows how to decompose the problem into two stages: one that is embarrassingly parallel and one that is amenable to MWU. We then model the cast of each MWU variant and derive the conditions under which it is likely to be preferred in practice. We find that all three MWU algorithms achieve accuracy above 90% but that there are significant differences in runtime and total cost. When 90% accuracy is sufficient and evaluating options is expensive, such as in our use case, we find that the algorithm that uses global memory and has high communication cast outperforms the other two. We analyze the reasons for this surprising result.

关键词： distributed processing Runtime Heuristic algorithms software algorithms Computer bugs Stochastic processes Maintenance engineering

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Maintaining Fuse in the Presence of distributed Generation Sources in the Distribution Network to Improve Protection System 17

Maintaining Fuse in the Presence of Distributed Generation S...

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17th IEEE international symposium on Applied Computational Intelligence and Informatics, SACI 2023

作者： Taleb, Mahdi Fani, Bahador Shahgholian, Ghazanfar Mosavi, Amir Fathollahi, Arman Islamic Azad University Najafabad Branch Department of Electrical Engineering Najafabad Iran Islamic Azad University Smart Microgrid Research Center Najafabad Branch Najafabad Iran Obuda University John von Neumann Faculty of Informatics Budapest Hungary Aarhus University Department of Electrical and Computer Engineering Aarhus Denmark

ISBN: (纸本)9798350321104

The need to reduce greenhouse gas emissions and the high price of fossil fuels have made renewable resources attractive in energy-based economies around the world. Renewable energy sources will make up a significant part of the modern energy system in the future because they have promising potential. Many countries are already working to increase their capacity for renewable energy. The placement of renewable resources in power systems has gained significant attention due to their potential to provide power to distribution feeders or near consumers. However, the integration of these resources can have adverse effects on the distribution network, which necessitates their placement to be carefully considered. In this study, a novel method of coordinating protection devices based on the current control of distributed production sources using their current-voltage diagram during fault conditions is suggested. The proposed method aims to address the coordination and regulation problems encountered when integrating scattered resources into the network. To evaluate the effectiveness of our approach, we compare the impact of the presence of renewable sources at various points on the network during flooding. We conduct simulations using the ETAP software and present the obtained results. The proposed protection coordination method effectively mitigates the challenges of integrating renewable resources into the distribution network, providing a promising solution to support the transition towards a more sustainable energy future. © 2023 IEEE.

关键词： Renewable energy resources

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A Protection Methodology for Supporting distributed Generations with Respect to Transient Instability 17

A Protection Methodology for Supporting Distributed Generati...

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17th IEEE international symposium on Applied Computational Intelligence and Informatics, SACI 2023

作者： Taheri, Milad Shahgholian, Ghazanfar Fani, Bahador Mosavi, Amir Fathollahi, Arman Department of Electrical Engineering Najafabad Branch Islamic Azad University Najafabad Iran Islamic Azad University Smart Microgrid Research Center Najafabad Branch Najafabad Iran Obuda University John von Neumann Faculty of Informatics Budapest Hungary Aarhus University Department of Electrical and Computer Engineering Aarhus Denmark

ISBN: (纸本)9798350321104

The reliable and stable operation of electrical power systems is crucial for maintaining power delivery to end-users. Transient stability analysis is a fundamental aspect of power system protection. In addition, the increased penetration of distributed generations (DGs) into the power system creates new challenges for protection devices. Thus, understanding the impact of DGs on power system protection is critical. This study investigates the transient stability of a power distribution grid with DGs to determine the optimal protection settings for overcurrent relays (OCRs). Specifically, the study aims to propose a novel technique for calculating the modified value of the overcurrent relay time dial setting (TDS) to ensure the coordination of OCRs and prevent the instability of DG sources. The transient stability of DGs is evaluated under various fault locations and DG penetration rates. The constraints imposed by DGs are then considered in the protection coordination of OCRs. The proposed protection strategy maintains coordination between the overcurrent relays and prevents the instability of DG sources. To validate the effectiveness of the proposed protection strategy, numerical simulations are conducted using ETAP software on an IEEE 33-bus distribution system. The simulation results show that the proposed method can achieve reliable protection coordination of OCRs and maintain the stability of DG sources. Finally, to enhance the proposed method's practicality and efficiency, future research directions are identified, including the integration of artificial intelligence (AI) techniques into the proposed protection strategy. These efforts would consider the practical constraints and enable the proposed method's scalability and applicability to larger and more complex power systems. © 2023 IEEE.

关键词： Artificial intelligence

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EIF: A Mediated Pass-Through Framework for Inference as a Service 21

EIF: A Mediated Pass-Through Framework for Inference as a Se...

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21st IEEE international symposium on parallel and distributed Processing with Applications, 13th IEEE international Conference on Big Data and Cloud Computing, 16th IEEE international Conference on Social Computing and Networking and 13th international Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Gao, Yiming Wang, Zhen Wu, Weili Lam, Herman University of Florida Department of Electrical & Computer Engineering GainesvilleFL United States The University of Texas at Dallas Department of Electrical & Computer Engineering RichardsonTX75080 United States The University of Texas at Dallas Department of Computer Science RichardsonTX75080 United States

ISBN: (纸本)9798350329223

In order to effectively provide INaaS (Inference-as-a-Service) for AI applications in resource-limited cloud environments, two major challenges must be overcome: achieving low latency and providing multi-tenancy. This paper presents EIF (Efficient INaaS Framework), which uses a heterogeneous CPU-FPGA architecture to provide three methods to address these challenges (1) spatial multiplexing via software-hardware co-design virtualization techniques, (2) temporal multiplexing that exploits the sparsity of neural-net models, and (3) streaming-mode inference which overlaps data transfer and computation. The prototype EIF is implemented on an Intel PAC (shared-memory CPU-FPGA) platform. For evaluation, 12 types of DNN models were used as benchmarks, with different size and sparsity. Based on these experiments, we show that in EIF, the temporal multiplexing technique can improve the user density of an AI Accelerator Unit from 2 × to 6 × , with marginal performance degradation. In the prototype system, the spatial multiplexing technique supports eight AI Accelerators Unit on one FPGA. By using a streaming mode based on a Mediated Pass-Through architecture, EIF can overcome the FPGA on-chip memory limitation to improve multi-tenancy and optimize the latency of INaaS. To further enhance INaaS, EIF utilizes the MapReduce function to provide a more flexible QoS. Together with the temporal/spatial multiplexing techniques, EIF can support 48 users simultaneously on a single FPGA board in our prototype system. In all tested benchmarks, cold-start latency accounts for only approximately 5% of the total response time. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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A Few-shot Learning Approach for Anomaly Detection in Cooperative Tracking Control System 21

A Few-shot Learning Approach for Anomaly Detection in Cooper...

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作者： Rong, Jieqi Liu, Weirong Yu, Xiaoquan Ren, Wanwan Shu, Boyu Peng, Jun Central South University Computer Science and Engineering Changsha China Central South University School of Traffic and Transportation Engineering Changsha China Central South University School of Automation Changsha China

ISBN: (纸本)9798350329223

Cooperative tracking control for multiple heavy-haul trains can improve the safety and efficiency of heavy-haul railway transportation. However, the false data injection attack for the tracking control system is a serious obstacle, which will lead to imprecise train operation control. To address the issue, a few-shot learning approach for false data injection attack (FDIA) detection for tracking control systems of heavy-haul trains is proposed. First, the physical-cyber model of the tracking control system is established, and the affects of the FDIA on the tracking control system is analyzed. Second, the heavy-haul train anomaly detection simulation model was built by Matlab/Simulink to obtain the dataset for training the GAN model. Third, the Generative Adversarial Network (GAN) method is introduced to augment the anomaly dataset, which can solve the problem of lack of actual operation fault data of heavy trains. Further, the Local Outlier Factor (LOF) algorithm is leveraged to achieve the false data injection attack detection. Simulation results show that the proposed method can effectively increase the anomaly detection accuracy than other supervised methods. © 2023 IEEE.

关键词： Anomaly detection

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Auction-Based Job Scheduling for Smart Manufacturing 17

Auction-Based Job Scheduling for Smart Manufacturing

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17th IEEE international symposium on Applied Computational Intelligence and Informatics, SACI 2023

作者： Gatial, Emil Balogh, Zoltan Dolatabadi, Sepideh Hassankhani Ghorbel, Hatem Carrino, Stefano Dreyer, Jonathan Montequin, Vicente Rodriguez Gligor, Adrian Iantovics, Laszlo Barna Institute of Informatics Sav Parallel and Distributed Computing Bratislava Slovakia Institute of Informatics Sav Modelling and Control of Discrete Processes Bratislava Slovakia Haute École Arc Ingénierie HES-SO St-Imier Switzerland University of Oviedo Project Engineering Area Oviedo Spain University of Medicine Pharmacy Science and Technology of Targu Mures Electrical Engineering and Information Technology 'George Emil Palade' Targu Mures Romania

ISBN: (纸本)9798350321104

The paper describes a real-time job scheduling method designed for production of goods with different characteristics on machines with different processing parameters. The objective is to maximize the global reward of the factory as a sum of the rewards for each machine job. Traditionally, the task assignment problems deal with the assignment of m-tasks to n-agents and are calculated by analytical methods or heuristics. The proposed method is based on online auctions that distributes the tasks to the machines using the software agents. The method is implemented using the ERTS (Erlang Real-Time System) that allows adopting the features of fault-tolerance and real-time processing. The paper starts with introduction and review of related state-of-the-art. The following chapter briefly describes the problem and the specific requirements. The following chapter describes the software architecture of the online auction system, the use-case and the motivation for developing this method. The proposed method auction-based task assignment and its implementation are described in the next chapters. At the end of the work, the results of the method are presented in comparison with the optimal solution and the performance characteristics are also described. In the conclusion, possible advances and future work are proposed. © 2023 IEEE.

关键词： Fault tolerance

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Towards Efficient Prediction of Computing Resource Usage Using Deep Learning Techniques 16

Towards Efficient Prediction of Computing Resource Usage Usi...

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16th IEEE international symposium on Applied Computational Intelligence and Informatics, SACI 2022

作者： Schuszter, Ioan Cristian Cioca, Marius University of Petrosani Department of Computer and Electrical Engineering Romania Lucian Blaga University of Sibiu Department of Industrial Engineering and Management Romania

ISBN: (数字)9781665481250

ISBN: (纸本)9781665481250

In this paper we present the benefit of using deep learning time-series analysis techniques in order to reduce computing resource usage, with the goal of having more sustainable data centers. Modern enterprises and agile ways-of-working have led to a complete revolution of the way that software engineers develop and deploy software, with the proliferation of container-based technology such as Kubernetes and Docker. Modern systems tend to use up a large amount of resources even when idle, and intelligent scaling is one of the methods that could be used to prevent waste. We present several methods of predicting computer resource usage based on historical data of real production distributed software systems at the European Organization for Nuclear Research (CERN), enabling down-scaling the number of machines running a certain service during periods that have been identified as idle. The method leverages recurring neural network architectures in order to accurately predict the CPU future usage of a software system given its past activity. © 2022 IEEE.

关键词： Forecasting

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Optimizing Secure software Development Lifecycle with parallel Temporal Convolutional Network for Vulnerability Detection

Optimizing Secure Software Development Lifecycle with Parall...

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distributed systems, Computer Networks and Cybersecurity (ICDSCNC), international Conference on

作者： S. Padmakala Mohammad Naseera Sudhakar K Hassan Mohamed Ali Banoth Ramesh Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of CSE Malla Reddy Engineering College for Women (UGC-Autonomous) Hyderabad India Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Smart contracts are one of the software application which is developed based on blockchain, are extensively used in Internet of Things, finance management, and other applications. However, development of smart contracts increases, meanwhile vulnerability attacks elated to the software also increases which occur often. To overcome this limitation, parallel Temporal Convolutional Network (PTCN) is proposed for the vulnerability detection in secure software development. By using parallel temporal convolutions, the detection model process multiple time steps at the same time, that helps to identify patterns effectively. Firstly, Smartbugs datasets are used for acquiring data regarding the vulnerable behaviors in a software development. After that these data are pre-processed by regularization method and fed to word embedding for converting text data to vector data. Finally, from these vector data, the proposed PTCN model detects the vulnerability efficiently. The experimental results of the proposed PTCN method attained accuracy of 93.26% which is higher than the existing detection methods such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN).

关键词： Support vector machines Convolutional codes Accuracy Smart contracts software algorithms Vectors software Data models Convolutional neural networks software development management

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