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检索条件"任意字段=IEEE Data Compression Conference"

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Efficiency of data Preprocessing Application for Geographically Distributed Cyberphysical Systems Using WSNs

Efficiency of Data Preprocessing Application for Geographica...

引用

2024 International Russian Automation conference, RusAutoCon 2024

作者： Klimenko, Anna Safronenkova, Irina Institute of It and Security Technologies of Russian State University for Humanities Moscow Russia

The concepts of fog and edge computing are currently applied in the construction of distributed cyber-physical systems in order to reduce the load on the network infrastructure, as well as to reduce the time of data processing. In this case, as a rule, the process of workload distribution on fog layer nodes at fixed labor intensity of computational tasks is investigated. In this study, the impact of the amount of data preprocessing on edge devices with sending the results to the data collector node are considered, taking into account the transit overhead costs. We propose a formalization in the form of an additional parameter, the type of task, which determines the dependence of the amount of data to be sent on the labor intensity. As an example, data on data compression rate and efficiency for the gzip archiver is used to generate discrete types of tasks. The modeling results allow us to draw a conclusion about the influence of the choice of the type of data processing algorithm on the key criteria of the system. © 2024 ieee.

关键词： Cybersecurity

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Multi-Context Dual Hyper-Prior Neural Image compression 22

Multi-Context Dual Hyper-Prior Neural Image Compression

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22nd ieee International conference on Machine Learning and Applications, ICMLA 2023

作者： Khoshkhahtinat, Atefeh Zafari, Ali Mehta, Piyush M. Akyash, Mohammad Kashiani, Hossein Nasrabadi, Nasser M. West Virginia University Dept. of Computer Science & Electrical Engineering WV United States West Virginia University Dept. of Mechanical & Aerospace Engineering WV United States

ISBN: (纸本)9798350345346

Transform and entropy models are the two core components in deep image compression neural networks. Most existing learning-based image compression methods utilize convolutional-based transform, which lacks the ability to model long-range dependencies, primarily due to the limited receptive field of the convolution operation. To address this limitation, we propose a Transformer-based nonlinear transform. This transform has the remarkable ability to efficiently capture both local and global information from the input image, leading to a more decorrelated latent representation. In addition, we introduce a novel entropy model that incorporates two different hyperpriors to model cross-channel and spatial dependencies of the latent representation. To further improve the entropy model, we add a global context that leverages distant relationships to predict the current latent more accurately. This global context employs a causal attention mechanism to extract long-range information in a content-dependent manner. Our experiments show that our proposed framework performs better than the state-of-the-art methods in terms of rate-distortion performance. © 2023 ieee.

关键词： Attention Entropy model Global context Hyper-prior Neural image compression Transformer

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Lossy all-to-all exchange for accelerating parallel 3-D FFTs on hybrid architectures with GPUs

Lossy all-to-all exchange for accelerating parallel 3-D FFTs...

引用

ieee International conference on Cluster Computing (CLUSTER)

作者： Cayrols, Sebastien Li, Jiali Bosilca, George Tomov, Stanimire Ayala, Alan Dongarra, Jack Univ Tennessee Innovat Comp Lab Knoxville TN 37996 USA Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (数字)9781665498562

ISBN: (纸本)9781665498562

In the context of parallel applications, communication is a critical part of the infrastructure and a potential bottleneck. The traditional approach to tackle communication challenges consists of redesigning algorithms so that the complexity or the communication volume is reduced. However, there are algorithms like the Fast Fourier Transform (FFT) where reducing the volume of communication is very challenging yet can reap large benefit in terms of time-to-completion. In this paper, we revisit the implementation of the MPI all-to-all routine at the core of 3D FFTs by using advanced MPI features, such as One-Sided Communication, and integrate data compression during communication to reduce the volume of data exchanged. Since some compression techniques are 'lossy' in the sense that they involve a loss of accuracy, we study the impact of lossy compression in heFFTe, the state-of-the-art FFT library for large scale 3D FFTs on hybrid architectures with GPUs. Consequently, we design an approximate FFT algorithm that trades off user-controlled accuracy for speed. We show that we speedup the 3D FFTs proportionally to the compression rate. In terms of accuracy, comparing our approach with a reduced precision execution, where both the data and the computation are in reduced precision, we show that when the volume of communication is compressed to the size of the reduced precision data, the approximate FFT algorithm is as fast as the one in reduced precision while the accuracy is one order of magnitude better.

关键词： Three-dimensional displays Graphical models Fast Fourier transforms Focusing data compression Computer architecture Approximation algorithms

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Differential Lightweight Processing in Digital Twin construction of substations 3

Differential Lightweight Processing in Digital Twin construc...

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3rd International conference on Energy and Electrical Power Systems (ICEEPS)

作者： Zhang, Meng Lin, Ding Ding, Jing Fang, Tao Fuzhou Univ Key Lab Spatial Data Min & Informat Sharing Minist Educ Fuzhou 350108 Fujian Peoples R China State Grid Hangzhou Power Supply Co State Grid ZHEJIANG Elect Power Co Ltd Hangzhou 310000 Zhejiang Peoples R China

ISBN: (纸本)9798350375145;9798350375138

This study aims to address the challenge of lightweighting point cloud data at the part level in substation equipment, aligning with the requirements of digital twin applications for grid substations and considering the hierarchical structure of equipment. Utilizing the importance of equipment parts, we construct a priority queue and integrate the Draco algorithm to automatically lightweight transformer equipment point cloud into semantic models with multiple levels of detail at the part level. This approach effectively resolves the lightweighting issue of point cloud data for substation equipment at the part level, achieving a differential treatment where key parts maintain high original data retention rates while minor parts occupy fewer resources. In comparison to applying the global Draco algorithm to equipment, our method offers the advantage of flexibly allocating storage and transmission resources to user-focused key targets while keeping the overall compression rate unchanged.

关键词： lightweight substations equipment Draco algorithm hierarchical structure

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UniPTS: A Unified Framework for Proficient Post-Training Sparsity

UniPTS: A Unified Framework for Proficient Post-Training Spa...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xie, Jingjing Zhang, Yuxin Lin, Mingbao Lin, Zhihang Cao, Liujuan Ji, Rongrong Xiamen Univ Sch Informat Key Lab Multimedia Trusted Percept & Efficient Co Minist Educ China Xiamen Peoples R China Tencent Youtu Lab Shenzhen Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Post-training Sparsity (PTS) is a recently emerged avenue that chases efficient network sparsity with limited data in need. Existing PTS methods, however, undergo significant performance degradation compared with traditional methods that retrain the sparse networks via the whole dataset, especially at high sparsity ratios. In this paper, we attempt to reconcile this disparity by transposing three cardinal factors that profoundly alter the performance of conventional sparsity into the context of PTS. Our endeavors particularly comprise (1) A base-decayed sparsity objective that promotes efficient knowledge transferring from dense network to the sparse counterpart. (2) A reducing-regrowing search algorithm designed to ascertain the optimal sparsity distribution while circumventing overfitting to the small calibration set in PTS. (3) The employment of dynamic sparse training predicated on the preceding aspects, aimed at comprehensively optimizing the sparsity structure while ensuring training stability. Our proposed framework, termed UniPTS, is validated to be much superior to existing PTS methods across extensive benchmarks. As an illustration, it amplifies the performance of POT, a recently proposed recipe, from 3.9% to 68.6% when pruning ResNet-50 at 90% sparsity ratio on ImageNet. We release the code of our paper at https://***/xjjxmu/UniPTS.

关键词： model compression Post-training sparsity

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Research on Intelligent Balance Mechanism between Real-Time and Security in Unstructured data Transmission 6

Research on Intelligent Balance Mechanism between Real-Time ...

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6th ieee International conference on Civil Aviation Safety and Information Technology, ICCASIT 2024

作者： Honghan, Jin Yi, Ding Donghui, Tong Lisha, Zhang Xin, Deng Liaoning Electric Power Energy Development Group Co Ltd. Shenyang China

ISBN: (纸本)9798350389418

This paper focuses on the storage and transmission of unstructured information in the power grid based on ultra-bandwidth technology, aiming to optimize the efficiency of data management in the strong isolation environment of the power Internet region. For unstructured data objects such as pictures, documents, audio/video, etc., this paper deeply analyzes the current challenges in security, transmission quality and real-time, and reveals the transmission bottleneck of data under strong isolation environment. In order to solve these key problems, this paper innovatively designs an unstructured data management platform system that integrates ultra-bandwidth communication and distributed storage technology. The core of the system adopts an efficient data compression and encryption algorithm, combined with the ultra-bandwidth transmission channel, which significantly improves the transmission speed and security of unstructured data. In addition, by introducing the intelligent cache mechanism and dynamic data synchronization strategy, the system can automatically adjust the cache strategy and synchronization frequency according to the network condition and data access mode, and further optimize the real-time and efficiency of data transmission. Model simulation results show that the proposed system can significantly improve the transmission speed and real-time performance of unstructured data based on ensuring data security, especially in the strong isolation environment. By comparing different implementation methods, this paper finds that the scheme combining ultra-bandwidth and intelligent cache mechanism can effectively reduce transmission delay and improve the accuracy of data synchronization when dealing with large-scale unstructured data, which provides a strong technical support for efficient data management in the power Internet region. © 2024 ieee.

关键词： data accuracy

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F-TADOC: FPGA-Based Text Analytics Directly on compression with HLS 40

F-TADOC: FPGA-Based Text Analytics Directly on Compression w...

引用

40th ieee International conference on data Engineering, ICDE 2024

作者： Zhou, Yanliang Zhang, Feng Lin, Tuo Huang, Yuanjie Long, Saiqin Zhai, Jidong Du, Xiaoyong Renmin University of China China Advanced Micro Device United States Jinan University China Tsinghua University China

ISBN: (纸本)9798350317152

With the development of loT and edge computing, data analytics on edge has become popular, and text analytics directly on compression (TADOC) has been proven to be a promising technology for edge data analytics. At the same time, Field Programmable Gate Array (FPGA) also has broad application prospects in data analytics systems. Unfortunately, there is no work to date showing how to support TADOC using FPGAs. We propose FPGA-based text analytics directly on compression with HLS, namely F -TADOC, which is the first framework using HLS to provide FPGA-based text analytics directly on compressed data. It effectively supports efficient text analytics on FPGA without decompressing input data. F-TADOC addresses three major challenges. First, TADOC involves a large number of dependencies with unbalanced workload of rules, which causes extremely low pipeline efficiency on FPG As. To solve it, we use layer-wise approach to traverse the DAG composed of rules and allocate different pipeline processing strategies for rules of different sizes. Second, the data volume required can be large that beyond the on-chip memory capacity of FPGAs. We develop a memory pool supporting hash structure and on-chip caches on FPGA to deal with this challenge. Third, when traversing the DAG, there are massive indirect addressing with a large number of random accesses. This leads to redundant time overhead caused by the latency in accessing the High Bandwidth Memory (HBM) during the pipeline. We optimize the F -TADOC algorithm by using dataflow to expand the nested loop, thus eliminate indirect addressing. With four widely used datasets, experiments show that F -TADOC achieves 4.63 x and 1.49 x performance speedup over TADOC and G-TADOC. © 2024 ieee.

关键词： Field programmable gate arrays (FPGA)

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Toward Scalable and Efficient Visual data Transmission in 6G Networks 15

Toward Scalable and Efficient Visual Data Transmission in 6G...

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15th International conference on Information and Communication Technology Convergence, ICTC 2024

作者： Cai, Junhao An, Taegun Joo, Changhee

ISBN: (纸本)9798350364637

6G network technology will emerge in a landscape where visual data transmissions dominate global mobile traffic and are expected to grow continuously, driven by the increasing demand for AI-based computer vision applications. This will make already challenging task of visual data transmission even more difficult. In this work, we review effective techniques for visual data transmission, such as content compression and adaptive video streaming, highlighting their advantages and limitations. Further, considering the scalability and cost issues of cloud-based and on-device AI services, we explore distributed in-network computing architecture like fog-computing as a direction of 6G networks, and investigate the necessary technical properties for the timely delivery of visual data. © 2024 ieee.

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ICSE 2024 - Proceedings of the 46th ieee/ACM International conference on Software Engineering

ICSE 2024 - Proceedings of the 46th IEEE/ACM International C...

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46th ieee/ACM International conference on Software Engineering, ICSE 2024

ISBN: (纸本)9798400702174

The proceedings contain 68 papers. The topics discussed include: a large-scale survey on the usability of ai programming assistants: successes and challenges;LogShrink: effective log compression by leveraging commonality and variability of log data;how to support ML end-user programmers through a conversational agent;kind controllers and fast heuristics for non-well-separated GR(1) specifications;domain knowledge matters: improving prompts with fix templates for repairing python type errors;EDEFuzz: a web API Fuzzer for excessive data exposures;large language models are few-shot summarizers: multi-intent comment generation via in-context learning;modularizing while training: a new paradigm for modularizing DNN models;FAIR: flow type-aware pre-training of compiler intermediate representations;prompting is all you need: automated android bug replay with large language models;and do automatic test generation tools generate flaky tests?.

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data-Free Model Pruning at Initialization via Expanders

Data-Free Model Pruning at Initialization via Expanders

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2023 ieee/CVF conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Stewart, James Michieli, Umberto Ozay, Mete Samsung Research United Kingdom

ISBN: (纸本)9798350302493

In light of the enormous computational resources required to store and train modern deep learning models, significant research has focused on model compression. When deploying compressed networks on remote devices prior to training them, a compression scheme cannot use any training data or derived information (e.g., gradients). This leaves only the structure of the network to work with, and existing literature on how graph structure affects network performance is scarce. Recently, expander graphs have been put forward as a tool for sparsifying neural architectures. Unfortunately, however, existing models can rarely outperform a naïve random baseline. In this work, we propose a stronger model for generating expanders, which we then use to sparsify a variety of mainstream CNN architectures. We demonstrate that accuracy is an increasing function of expansion in a sparse model, and both analyse and elucidate its superior performance over alternative models. © 2023 ieee.

关键词： Deep learning

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