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检索条件"任意字段=IEEE International Conference on Distributed Computing Systems"

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RCIF: TOWARDS ROBUST distributed DNN COLLABORATIVE INFERENCE UNDER HIGHLY LOSSY NETWORKS 49

RCIF: TOWARDS ROBUST DISTRIBUTED DNN COLLABORATIVE INFERENCE...

引用

49th ieee international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Cheng, Yujun Zhang, Zhewei Wang, Shengjin Tsinghua Univ Dept Elect Engn Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China ZEEWAIN Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Collaborative Inference is a prospective paradigm for accelerating Deep Neural Network (DNN) inference by harnessing the computational resources of multiple devices. However, in highly lossy network environments, such as those encountered in wireless communication systems, the transmission loss of intermediate feature maps between devices can result in significant degradation of co-inference accuracy. In this paper, we first conduct a comprehensive investigation into the impact of intermediate feature map loss in real-world wireless scenarios and provide an in-depth analysis of loss patterns under UDP transmission. Motivated by these observations, we introduce Robust Co-inference Framework (RCIF), a novel framework that employs a hierarchical mask strategy to selectively drop activations at two different scales of feature maps. This approach enhances the robustness of DNN co-inference in the presence of network losses. Our evaluation on a variety of datasets and network architectures demonstrates that RCIF significantly enhances the accuracy and robustness of distributed DNN co-inference under highly lossy network conditions. Specifically, our results show that RCIF can achieve up to a 659% increase in accuracy compared to the original model under particularly poor network conditions.

关键词： Collaborative Inference Neural Networks Lossy Transmission Deep Learning Robustness

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Load-Aware Resource Scheduling in Fog computing Based Delay-Sensitive IoT Networks

Load-Aware Resource Scheduling in Fog Computing Based Delay-...

引用

2023 international conference on Smart Applications, Communications and Networking, SmartNets 2023

作者： Rajya Lakshmi, L. Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani India

ISBN: (纸本)9798350302523

Internet of Things (IoT), with its capability to connect anything, anywhere, anytime, bringing in revolutionary changes in data collection, processing and utilization. IoT networks generate a huge amount of data, which is difficult to process and store on IoT devices. Cloud computing with its huge availability of resources, can process and store these huge volumes of data. However, owing to the long distance between the cloud and IoT devices, the cloud computing suffers the issues like high latency, network congestion and unreliability. To address these issues, fog computing, which is a decentralized computing paradigm has been proposed as an extension to cloud computing. To utilize the fog resources efficiently and to avoid over-utilization and under-utilization of resources, this paper proposes a resource scheduling algorithm which is capable of balancing the load on fog nodes while satisfying the delay requirements of IoT applications. Extensive simulations are conducted to evaluate the performance of the proposed method. The simulation results establish that the load is almost equally distributed among the fog nodes in the network in most of the traffic arrival scenarios. © 2023 ieee.

关键词： Fog computing

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A Customizable WebRTC-based Video Conferencing System For Real-time Communication 31

A Customizable WebRTC-based Video Conferencing System For Re...

引用

31st international conference on systems, Signals and Image Processing (IWSSIP)

作者： Tursunov, Javlon Rozinaj, Gregor Dwivedi, Vivek Minarik, Ivan Slovak Univ Technol Bratislava Inst Multimedia Informat & Commun Technol Bratislava Slovakia

ISBN: (纸本)9798350391893;9798350391886

In the dynamic landscape of modern communication, the demand for innovative virtual video conferencing solutions is ever-increasing. Our work presents an innovative approach to building a virtual video conferencing system that can be used by remote users with the help of a web page. Our system allows remote participants, joining via a web browser, to freely navigate and view the virtual environment from any angle, enhancing spatial awareness and engagement. Additionally, our system grants participants the freedom to view the environment independently, even if the host restricts certain views which is one of the main drawbacks of the current video conferencing systems. Unlike current platforms, our solution also allows users to choose their appearance location within the virtual space and this feature is missing in the current systems. Furthermore, our system is highly customizable, enabling the integration of features such as recording specific portions of the screen, which are not available in existing video conferencing tools. This flexibility ensures a more immersive, interactive, and personalized meeting experience, significantly advancing the capabilities of remote collaboration technologies. Our work highlights the results of research carried out to create a virtual conference setting in the Unity environment and establish successful real-time communication between the webpage and the Unity environment. In this virtual setting, monitors act as participants, and participants can choose on which monitor they want to appear. Participants join this virtual meeting setup from a webpage, which consists of two windows: the first window shows the participants themselves, and the second window displays the virtual meeting setup. Participants can observe this environment from any perspective they want, navigating using a keyboard and a mouse. Since we are implementing everything from scratch, we have full control over every feature and functionality with Agora video SDK

关键词： virtual conferencing WebRTC real-time communication metaverse agora social presence metaverse computing token network performance

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Deep Learning Compiler Optimization on Multi-Chiplet Architecture 5

Deep Learning Compiler Optimization on Multi-Chiplet Archite...

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5th ieee international conference on Artificial Intelligence Circuits and systems, AICAS 2023

作者： Xu, Huiqing Mao, Kuang Pan, Quihong Tang, Zhaorong Wang, Mengdi Wang, Ying Zhejiang Lab Research Center for Advanced Computing Systems Hangzhou China Chinese Academy of Sciences Institute of Computing Technology Beijing China

ISBN: (纸本)9798350332674

Multi-chiplet architecture can provide a high-performance solution for new tasks such as deep learning models. In order to fully utilize chiplets and accelerate the execution of deep learning models, we present a deep learning compilation optimization framework for chiplets, and propose a scheduling method based on data dependence. Experiments show that our method improves the compilation efficiency, and the performance of the scheduling scheme is at least 1-2 times higher than the traditional algorithms. © 2023 ieee.

关键词： Deep learning

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Demand Response Scheme: Achieving Sustainability in distributed Data Processing 9

Demand Response Scheme: Achieving Sustainability in Distribu...

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9th ieee international conference on Data Science in Cyberspace, DSC 2024

作者： Zhai, Xueying Peng, Yunfeng Guo, Xiuping Zhang, Liang Zhang, Wei University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Beijing University of Posts and Telecommunications School of Economics and Management Beijing China Guizhou Institute of Technology Guizhou Key Laboratory of Electric Power Big Data Guizhou China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. Data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization. © 2024 ieee.

关键词： Low emission

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Parallel computing with GPU: An accelerator for Data-Centric High Performance computing 1

Parallel Computing with GPU: An accelerator for Data-Centric...

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1st international conference on Innovative Engineering Sciences and Technological Research, ICIESTR 2024

作者： Siddique, Mohammed Ashour, Mohammed Waleed Majan University College Faculty of Information Technology Muscat Oman

ISBN: (纸本)9798350348637

In recent years, the demand for high-performance computing in various disciplines of ICTs has affected the dependency on single core processes due to its decline in efficiency. Whereas, this requirement of advance in processing power has seen the emergence of Graphical Processing Units (GPU). In this paper, the role of GPUs in parallel computing along with its advantages, limitations and performance in various domains have been presented. The massive parallelism feature of GPUs can break down complex problems into smaller bits of computation with greater efficiency. The ability to compute complex distributed processes is ideal for implementing Machine Learning (ML) and Deep Learning (DL) algorithms. The evaluation of GPUs performance in medical physics, large scale data mining, CUDA workload analysis and applications of ML and DL has been deliberated on the high-performance computing. As GPUs continue to evolve, processing performance and efficiency should improve, and stand out as a cost-effective and powerful solution for optimizing faster and reliable large-scale data processing. Future research is expected to optimize GPU architectures for developing computing paradigms to keep them relevant in high-performance computing's fast-changing industry. © 2024 ieee.

关键词： Graphics processing unit

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Real-time pricing method through market auction based on multi-agent reinforcement learning 8

Real-time pricing method through market auction based on mul...

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8th international conference on Power and Renewable Energy, ICPRE 2023

作者： Hong, Peng He, Shuping Fang, Xiaohan Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Hefei230601 China

ISBN: (纸本)9798350328813

With the development of distributed energy systems, pricing different energy sources in microgrids has become a significant challenge. To solve this problem, this paper proposes an real-time pricing method through market auction based on multi-agent reinforcement learning. The energy supply side provides different energy production and quotations, while consumers on the energy demand side freely quote and purchase the required energy in the energy market based on their own needs. The final transaction price of different energy sources is determined by the quotes of the buyer and seller who have reached the transaction. The simulation results indicate that the proposed pricing method have a good performance. © 2023 ieee.

关键词： Market auction Multi-agent reinforcement learning Real-time pricing

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Benchmarking and Continuous Performance Monitoring of Ookami, an ARM Fujitsu A64FX Testbed Cluster

Benchmarking and Continuous Performance Monitoring of Ookami...

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2024 Workshops of the international conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Simakov, Nikolay A. White, Joseph P. Jones, Matthew D. Siegmann, Eva Wood, Daniel G. Coskun, Firat Harrison, Robert J. SUNY University at Buffalo Center for Computational Research BuffaloNY United States Stony Brook University Institute for Advanced Computational Science Stony BrookNY United States

ISBN: (纸本)9798350355543

Continuous performance monitoring is critical for maintaining optimal performance of High-Performance computing resources. This is especially important for technological test bed systems, in which software updates occur often, and performance degradation in one place can be masked by performance improvement in other places. This paper reports on our experience running continuous performance monitoring on Ookami, an ARM Fujitsu A64FX machine (the first ARM CPU with SVE-512 support) using XDMoD. After over three years of monitoring, we found that the applications and numerical library performance improved the most on the initial release with new technology support, followed by a series of smaller performance gains. Another interesting observation about numeric libraries is that the most invested vendors produce optimized code faster than community codes. © 2024 ieee.

关键词： Benchmarks High-Performance computing Performance Monitoring Regression Tests

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An Approach Towards distributed DNN Training on FPGA Clusters 37th

An Approach Towards Distributed DNN Training on FPGA Cluster...

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37th international conference on Architecture of computing systems (ARCS)

作者： Kreowsky, Philipp Knapheide, Justin Stabernack, Benno Heinrich Hertz Inst Nachrichtentech Berlin GmbH Fraunhofer Inst Telecommun Berlin Germany Univ Potsdam Embedded Syst Architectures Signal Proc Potsdam Germany

ISBN: (纸本)9783031661457;9783031661464

We present NADA, a Network Attached Deep learning Accelerator. It provides a flexible hardware/software framework for training deep neural networks on ethernet-based FPGA clusters. The NADA hardware framework instantiates a dedicated entity for each layer in a model. Features and gradients flow through these entities in a tightly pipelined manner. From a compact description of a model and target cluster, the NADA software framework generates specific configuration bitstreams for each particular FPGA in the cluster. We demonstrate the scalability and flexibility of our approach by mapping an example CNN onto a cluster consisting of three up to nine Intel Arria 10 FPGAs. To verify NADAs effectiveness for commonly used networks, we train MobileNetV2 on a six-node cluster. We address the inherent incompatibility of the tightly pipelined layer parallel approach with batch normalization by using online normalization instead.

关键词： FPGA Network Attached Accelerator MobileNetV2 CNN Layer Parallelism

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distributed Reactive Power Optimization for Active Distribution Network Based on Network Partition

Distributed Reactive Power Optimization for Active Distribut...

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2024 international conference on Electrical Power systems and Intelligent Control, EPSIC 2024

作者： Zhu, Jinzhou Jin, Qingren Cai, Xinlei Yu, Lei Meng, Zijie Liu, Tong Yu, Zhenfan Power Dispatching and Controlling Center Guangdong Power Grid Company Limited Guangdong Power Grid Company Limited Guangzhou China Guangzhou China

ISBN: (纸本)9798350366075

Active distribution networks (ADNs) serve as a linchpin in contemporary power systems, tasked with accommodating the rising penetration of renewable energy sources while ensuring efficient and reliable operation. This paper proposes a network partition-based framework for distributed reactive power optimization in active distribution networks, with the minimization of network losses during the operation of active distribution networks as the optimization objective. This paper constructs a distributed reactive power optimization model that includes both continuous and discrete devices, such as on-load tap changer (OLTC) and capacitor banks (CB). Moreover, a solution method based on the alternating optimization process (AOP) and alternating direction method of multipliers (ADMM) is introduced. Case study simulations validate the effectiveness of the proposed reactive power optimization algorithm. ©2024 ieee.

关键词： Power distribution networks

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