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Event-Triggered Switched Observers for Networked Distributed Multi-Agent systems via Cloud computing 6

Event-Triggered Switched Observers for Networked Distributed...

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6th international conference on Electronic Engineering and Informatics, EEI 2024

作者： Zhang, Chen Ren, Hongru Ma, Hui Zhou, Qi School of Automation Guangdong University of Technology Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China School of Mathematics and Statistics Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9798350353594

this paper designs event-triggered switched observers for the networked distributed multi-agent systems based on the cloud computing systems. Firstly, a novel cloud computing system architecture is designed, in which each group of agents can interact with each others through distributed cloud nodes, and each cloud node also serves as an event-triggered switched observer to observe the topologies of the systems. Secondly, event-triggered switched observers are designed, which can assist distributed agents in accurately obtaining information from the leader, even with continuous switching topologies. then, through stability analysis, we demonstrate the effectiveness of the observers. Finally, we prove the feasibility of the observers through simulation. © 2024 IEEE.

关键词： Distributed cloud

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Efficient FPGA-Based Transformer Accelerator Using In-Block Balanced Pruning 13

Efficient FPGA-Based Transformer Accelerator Using In-Block ...

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13th IEEE international conference on Communications, Circuits and systems (ICCCAS)

作者： Wang, Saiqun Zhang, Hao Ocean Univ China Informat Sci & Engn Qingdao Peoples R China

ISBN: (纸本)9798350386288;9798350386271

Recently, transformer models have been widely deployed in natural language processing and image processing. However, its superior performance comes with high amount of parameters and computations which make it difficult to deploy transformer models in resource limited devices. To reduce the computation cost of transformer models, in this paper, an improved network pruning method is proposed. In the proposed pruning method, the parameter matrix is decomposed into blocks of a specific size. then, pruning is applied to each block so that the number of parameters remaining in each block is the same. To further reduce the memory requirement of the parameters, an efficient memory storage pattern for sparse parameters is also proposed in this paper. Finally, by combining the proposed methods, an energy efficient transformer accelerator architecture is proposed. the proposed accelerator is implemented in FPGA devices and implementation results show that the proposed design can significantly improve the speed performance and energy efficiency when compared with previous designs.

关键词： Transformer Accelerator Network Pruning FPGA Energy-Efficient computing Sparse Storage Pattern

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EXARL-PARS: Parallel Augmented Random Search Using Reinforcement Learning at Scale for Applications in Power systems 14

EXARL-PARS: Parallel Augmented Random Search Using Reinforce...

引用

14th ACM international conference on Future Energy systems (e-Energy)

作者： Sharma, Himanshu Suetterlein, Joshua Lakshmiranganatha, Sumathi Flynn, thomas Vrabie, Draguna Sweeney, Christine Ramakrishnaiah, Vinay Pacific Northwest Natl Lab Richland WA 99352 USA Los Alamos Natl Lab Los Alamos NM USA Brookhaven Natl Lab Upton NY USA

ISBN: (纸本)9798400702273

With recent advances in deep learning and large-scale computing, learning-based controls have become increasingly attractive for complex physical systems. Consequently, developing generalized learning-based control software that takes into account the next generation of computing architectures is paramount. Specifically, for the case of complex control, we present the Easily eXtendable architecture for Reinforcement Learning (EXARL), which aims to support various scientific applications seeking to leverage reinforcement learning (RL) on exascale computing architectures. We demonstrate the efficacy and performance of the EXARL library for the scientific use case of designing a complex control policy to stabilize a power system after experiencing a fault. We use a parallel augmented random search method developed within EXARL and present its preliminary validation and performance stabilization of a fault for the IEEE 39-bus system.

关键词： Reinforcement learning augmented random search exascale computing power systems

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Mobile Edge Slice Broker: Mobile Edge Slices Deployment in Multi-Cloud Environments 7

Mobile Edge Slice Broker: Mobile Edge Slices Deployment in M...

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7th IEEE international conference on Fog and Edge computing (ICFEC) / 23rd IEEE/ACM international Symposium on Cluster, Cloud and Internet computing (CCGRID)

作者： Boutalbi, Samia Kheir, Nizar Koutsiamanis, Remous-Aris Sudholt, Mario Moussa, Yann Dan Ericsson R&D Ctr Massy France Amazon Web Serv Nanterre France IMT Atlantique STACK Team Nantes France

ISBN: (纸本)9798350322880

Harnessing the network slicing feature of the 5G architecture and the mobile cloud computing capabilities offered by Mobile Edge computing (MEC), mobile edge communication systems are emerging that contribute to the convergence of telecom and cloud services. In this scenario, mobile edge slices can be created and deployed by leveraging multi-cloud infrastructure. However, it becomes difficult for potential users, such as mobile edge slice tenants, to evaluate and select the Cloud Infrastructure Provider (CIP) which best meets their heterogeneous requirements. therefore, mobile edge systems require slice brokering to mediate with resource and infrastructure providers, so that mobile edge slice tenants can specify a single set of network and cloud service Key Performance Indicator (KPI) requirements. In this paper we present a new type of broker, the Mobile Edge Slice Broker, that allows designing, provisioning, and orchestrating the deployment and operation of end-to-end mobile edge slices over edge and multi-cloud environments, while maintaining dynamic monitoring, reconfiguration and optimization capabilities.

关键词： 5G Mobile Communications Network Slicing Mobile Edge computing Mobile Edge Slice Broker MultiTenancy Multi-Cloud.

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SDN-DMQTT: SDN-Based Platform for Re-configurable MQTT Distributed Brokers architecture 20th

SDN-DMQTT: SDN-Based Platform for Re-configurable MQTT Distr...

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20th EAI international conference on Mobile and Ubiquitous systems - computing Networking and Services (EAI MobiQuitous)

作者： Hmissi, Fatma Ouni, Sofiane ENSI Cristal Lab Manouba Univ Campus Manouba 2010 Tunisia

ISBN: (纸本)9783031639913;9783031639920

the MQTT (Message Queuing Telemetry Transport) protocol is widely recommended for IoT (Internet of things) communication. It uses a single broker for data exchange, which is typically placed in the cloud. However, this centralized approach is not effective for scaling, leading to mist and edge broker distribution. In distributed infrastructures, administrators are forced to manually reconfigure all MQTT communication to meet performance metrics. However, manual reconfiguration has several drawbacks, e.g., it is a time-consuming process and prone to error. To remedy these issues, this paper proposes combining Software-Defined Networking (SDN) and the MQTT protocol. this proposal enables dynamic reconfiguration of MQTT communication on distributed MQTT-based infrastructure. In the current paper, a realcase study of experiments on a real test-bed is carried out to justify the necessity of the proposed approach.

关键词： IoT SDN MQTT Dynamic Reconfiguration

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Enhancing CGRA Efficiency through Aligned Compute and Communication Provisioning 25

Enhancing CGRA Efficiency Through Aligned Compute and Commun...

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30th international conference on Architectural Support for Programming Languages and Operating systems-ASPLOS

作者： Li, Zhaoying Dangi, Pranav Yin, Chenyang Bandara, thilini Kaushalya Juneja, Rohan Tan, Cheng Bai, Zhenyu Mitra, Tulika Natl Univ Singapore Singapore Singapore Peking Univ Shanghai Peoples R China Google Houston TX USA

ISBN: (纸本)9798400706981

Coarse-grained Reconfigurable Arrays (CGRAs) are domain-wagnostic accelerators that enhance the energy efficiency of resource-constrained edge devices. the CGRA landscape is diverse, exhibiting trade-offs between performance, efficiency, and architectural specialization. However, CGRAs often overprovision communication resources relative to their modest computing capabilities. this occurs because the theoretically provisioned programmability for CGRAs often proves superfluous in practical implementations. In this paper, we propose Plaid, a novel CGRA architecture and compiler that aligns compute and communication capabilities, thereby significantly improving energy and area efficiency while preserving its generality and performance. We demonstrate that the dataflow graph, representing the target application, can be decomposed into smaller, recurring communication patterns called motifs. the primary contribution is the identification of these structural motifs within the dataflow graphs and the development of an efficient collective execution and routing strategy tailored to these motifs. the Plaid architecture employs a novel collective processing unit that can execute multiple operations of a motif and route related data dependencies together. the Plaid compiler can hierarchically map the dataflow graph and judiciously schedule the motifs. Our design achieves a 43% reduction in power consumption and 46% area savings compared to the baseline high-performance spatio-temporal CGRA, all while preserving its generality and performance levels. In comparison to the baseline energy-efficient spatial CGRA, Plaid offers a 1.4x performance improvement and a 48% area savings, with almost the same power.

关键词： Dataflow computing Coarse-Grained Reconfigurable Array (CGRA) Motifs

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Fog computing for Deep Learning with Pipelines 7

Fog Computing for Deep Learning with Pipelines

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7th IEEE international conference on Fog and Edge computing (ICFEC) / 23rd IEEE/ACM international Symposium on Cluster, Cloud and Internet computing (CCGRID)

作者： Vainio, Antero Mudvari, Akrit Kiedanski, Diego Tarkoma, Sasu Tassiulas, Leandros Univ Helsinki Helsinki Finland Yale Univ New Haven CT USA

ISBN: (纸本)9798350322880

In this article, we introduce a fog system design for processing data collected from edge devices, such as mobile, sensor, and extended (mixed, augmented, virtual) reality equipment. Our system enables the network to provide hardware-accelerated processors for resource-intensive computations on data gathered from remote locations, such as 5G and beyond mobile networks. By splitting heavy computations into pipelines, and distributing them among processors in the edge, fog and the cloud, our design benefits from the processing power of the cloud, while utilizing fog devices with a lower network latency. We implement our design, and use it for distributed training and inference with industry-grade deep learning models for computer vision. We deploy our architecture in infrastructure including cloud and edge servers supporting GPU-accelerated computations. We benchmark pipelines in various deployment settings to study the overhead that they introduce. Our contributions are a new design for wide-area data processing, a framework that realizes this design and provides means of developing applications that are optimized in terms of infrastructure and hardware. these contributions are complemented with our benchmark results, which reveal the potential causes of processing overhead.

关键词： fog computing stream processing systems deep learning

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Asynchronous Arbitration Circuit Optimization for Multicore Neuromorphic Processors 17

Asynchronous Arbitration Circuit Optimization for Multicore ...

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17th IEEE international conference on Solid-State and Integrated Circuit Technology, ICSICT 2024

作者： Guo, Jiajie Lin, Guangyao Wang, Bohan Yu, Zhiyi Xiao, Shanlin School of Microelectronics Science and Technology Sun Yat-sen University China Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems China

ISBN: (纸本)9798350361834

Spiking Neural Networks (SNNs), with their significant advantages of low power consumption and low latency, are a promising approach for edge computing and have attracted considerable attention in the scientific community. Multicore neuromorphic processors have been demonstrated as an effective solution for constructing large-scale SNN models. However, as the scale of these networks increases, the resource and power demands of inter-core communication escalate significantly. the inter-core arbiter represents an important component of the Power-Performance-Area (PPA) bottleneck. Compared to synchronous circuits, asynchronous circuits feature lower power consumption and enhanced robustness, making them more suitable for inter-core communication. We propose an asynchronous Quasi-Delay Insensitive (QDI) arbitration circuit based on hybrid encoding method and the NCL_X pipeline architecture, achieving a 22% improvement in speed and a 23% reduction in power consumption. © 2024 IEEE.

关键词： Edge computing

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CMLB: a Communication-aware and Memory Load Balance Mapping Optimization for Modern NUMA systems 23

CMLB: a Communication-aware and Memory Load Balance Mapping ...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Li, Jingbo Zhang, Yuxin Zhang, Xingjun Xi'an Jiaotong University High Performance Computing Lab Xi'an China

ISBN: (纸本)9781665494571

For parallel applications, mapping parallel threads to cores according to the access behavior plays an important role to optimize the applications performance. the imbalance between thread communication and memory bandwidth will severely increase the average latency and the execution time of the application when running on modern nonuniform memory access (NUMA) architecture. Previous studies on thread mapping mostly focus on the locality of memory accesses to improve the communication efficiency. However, maximizing the locality may cause memory congestion because of the imbalance on memory bandwidth between nodes. In this paper, a communication-aware and memory load balance mapping algorithm (CMLB) for modern NUMA systems is propose which works on improving the locality of communication as well as avoiding memory congestion problem. To verify the effectiveness of the algorithm, the applications from NAS parallel benchmark and Parsec benchmark are used. Experimental results show that CMLB could greatly balance the memory bandwidth between nodes to reduce the memory latency and also improve the locality of communication, get the better performance than the state-of-the-art mapping methods. © 2021 IEEE.

关键词： Instruction sets Memory management Memory architecture Bandwidth Benchmark testing Behavioral sciences Task analysis

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Distributed HBase Cluster Storage Engine and Database Performance Optimization 23

Distributed HBase Cluster Storage Engine and Database Perfor...

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作者： Zhang, Hao Rong-Li, G.A.I. School of Information Engineering Dalian University Dalian China

ISBN: (纸本)9781665494571

With the development of network society, individuals and enterprises generate massive data every day. However, the traditional relational MySQL database has poor performance in storing and processing massive data. In the case of huge amount of data, MySQL may even crash, which obviously cannot meet the needs of massive data storage. Obviously, with more and more emphasis on user-friendliness, the storage and processing application of massive data determine the future development of enterprises. this paper discusses the storage engine, high concurrency, architecture and distributed cluster of HBase, and then compares the performance differences between MySQL and HBase optimized with Bloom filter(Bloom HBase) on Linux platform. © 2021 IEEE.

关键词： Concurrent computing Smart cities Linux Distributed databases Memory Big Data Computer crashes

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