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

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pLPAQ: Accelerating LPAQ compression on FPGA 21

pLPAQ: Accelerating LPAQ Compression on FPGA

引用

21st International conference on Field-Programmable Technology (ICFPT)

作者： Tang, Dongdong Sun, Xuan Guan, Nan Kuo, Tei-Wei Xue, Chun Jason City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Natl Taiwan Univ Dept Comp Sci & Informat Engn New Taipei Taiwan

ISBN: (数字)9781665453363

ISBN: (纸本)9781665453363

In recent years, the demand for data storage space has increased dramatically due to the exponential growth of data volume. data compression is of great significance since it saves data storage space and reduces data transfer demand. compression algorithms based on statistical models have a much higher compression ratio than dictionary-based methods, but the high computational time cost of statistical modeling limits their wider application. In this paper, we introduce pLPAQ, an FPGA-based design of a powerful compression algorithm LPAQ based on statistical models. A novel hardware accelerator is proposed to speed up LPAQ by fully utilizing the parallelism of FPGA. Experimental results show that the proposed design can achieve a throughput of 12 MB/s on Xilinx Virtex Plus UltraScale XCVU9P card, 25x faster than executing on AMD Ryzen R7 4800U at 2.8 GHz and 80x faster compared with the naive FPGA implementation on average.

关键词： data compression FPGA LPAQ

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Real-time Monitoring System for Large-scale data Processing in Cloud Computing Environment: A Research Study

Real-time Monitoring System for Large-scale Data Processing ...

引用

2024 International conference on Information Technology, Communication Ecosystem and Management, ITCEM 2024

作者： Zhang, Jin Hubei Provincial Institute of Public Administration Party School of the CPC Hubei Provincial Committee Wuhan432200 China

ISBN: (纸本)9798331542696

To meet the real-time processing demands of largescale e-commerce transaction data, a distributed data processing system was designed and implemented. The system utilizes an improved LZ4 compression algorithm and microbatch processing mechanism to optimize data transmission efficiency and computing performance. A 25-node cluster environment was set up for performance testing, and the system achieved a single-node processing capacity of 56,700 transactions per second and an overall processing capacity of 89,000 transactions per second. After six months of deployment on a certain e-commerce platform, the system processed an average of 500 million transactions per day, with an 85% increase in accurate identification of abnormal orders and a system availability of 99.99%, achieving high-efficiency and stable data processing. © 2024 ieee.

关键词： data centers

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FPGA-based Radar Altimeter Pulse compression And Coherent Accumulation Implementation 19

FPGA-based Radar Altimeter Pulse Compression And Coherent Ac...

引用

19th International conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2023

作者： Na, Shengyuan Xie, Xihai Wang, Xuanbo Zhang, Mingxin Hui, Biao Xi'an University of Posts and Telecommunications School of Communications and Information Engineering Xi'an China

ISBN: (纸本)9798350304398

Coherent accumulation is an important technique to improve the detection capability of pulsed radar altimeter targets when range-walk and velocity ambiguity do not occur. This paper focuses on the pulse compression technique and the FPGA implementation of the coherent accumulation technique. As the radar signal requires high precision for processing data, and the precision of floating-point numbers is higher than that of fixed-point numbers in the process of hardware implementation. Therefore, the data types handled in this paper are all in the form of ieee 754 standard single-precision floating-point numbers. On this basis, a complex multiplication model for floating-point numbers is designed and improved in this paper, and the proposed complex multiplication module achieves a lower LUT resources and better Static Timing compared with the traditional complex multiplication operation. © 2023 ieee.

关键词： Field programmable gate arrays (FPGA)

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Compressing and Fine-tuning DNNs for Efficient Inference in Mobile Device-Edge Continuum

Compressing and Fine-tuning DNNs for Efficient Inference in ...

引用

ieee International Mediterranean conference on Communications and Networking (ieee MeditCom)

作者： Singh, Gurtaj Chukhno, Olga Campolo, Claudia Molinaro, Antonella Chiasserini, Carla Fabiana Univ Mediterranea Reggio Calabria Calabria Italy Consorzio Nazl Interuniv Telecomunicaz CNIT Genoa Italy Politecn Torino Turin Italy

ISBN: (纸本)9798350309492;9798350309485

Pruning deep neural networks (DNN) is a wellknown technique that allows for a sensible reduction in inference cost. However, this may severely degrade the accuracy achieved by the model unless the latter is properly fine-tuned, which may, in turn, result in increased computational cost and latency. Thus, upon deploying a DNN in resource-constrained edge environments, it is critical to find the best trade-off between accuracy (hence, model complexity) and latency and energy consumption. In this work, we explore the different options for the deployment of a machine learning pipeline, encompassing pruning, finetuning, and inference, across a mobile device requesting inference tasks and an edge server, and considering privacy constraints on the data to be used for fine-tuning. Our experimental analysis provides insights for an efficient allocation of the pipeline tasks across network edge and mobile device in terms of energy and network costs, as the target inference latency and accuracy vary. In particular, our results highlight that the higher the edge server load and the number of inference requests, the more convenient it becomes to deploy the entire pipeline at the mobile device using a pruned model, with a cost reduction of up to a factor two compared to deploying the whole pipeline at the edge.

关键词： Edge computing Edge-mobile device continuum Machine learning pipeline ML model compression

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Pulsation artifact removal from intra-operatively recorded local field potentials using sparse signal processing and data-specific dictionary

Pulsation artifact removal from intra-operatively recorded l...

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45th Annual International conference of the ieee-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Swamy, Chandra Prakash Besheli, Behrang Fazli Branco, Luciano R. F. Provenza, Nicole R. Sheth, Sameer A. Goodman, Wayne K. Viswanathan, Ashwin Ince, Nuri Firat Univ Houston Dept Biomed Engn Houston TX 77004 USA Baylor Coll Med Dept Neurosurgery Houston TX USA Baylor Coll Med Menninger Dept Psychiat & Behav Sci Houston TX USA

ISBN: (纸本)9798350324471

Neural recordings frequently get contaminated by ECG or pulsation artifacts. These large amplitude components can mask the neural patterns of interest and make the visual inspection process difficult. The current study describes a sparse signal representation strategy that targets to denoise pulsation artifacts in local field potentials (LFPs) recorded intraoperatively. To estimate the morphology of the artifact, we first detect the QRS-peaks from the simultaneously recorded ECG trace as an anchor point. After the LFP data has been epoched with respect to each beat, a pool of raw data segments of a specific length is generated. Using the K-singular value decomposition (K-SVD) algorithm, we constructed a data-specific dictionary to represent each contaminated LFP epoch in a sparse fashion. Since LFP is aligned to each QRS complex and the background neural activity is uncorrelated to the anchor points, we assumed that constructed dictionary will be formed to mainly represent the pulsation artifact. In this scheme, we performed an orthogonal matching pursuit to represent each LFP epoch as a linear combination of the dictionary atoms. The denoised LFP data is thus obtained by calculating the residual between the raw LFP and its approximation. We discuss and demonstrate the improvements in denoised data and compare the results with respect to principal component analysis (PCA). We noted that there is a comparable change in the signal for visual inspection to observe various oscillating patterns in the alpha and beta bands. We also see a noticeable compression of signal strength in the lower frequency band (<13 Hz), which was masked by the pulsation artifact, and a strong increase in the signal-to-noise ratio (SNR) in the denoised data.

关键词： Neurons

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A Novel Approach for Improving Image compression Ratio 3

A Novel Approach for Improving Image Compression Ratio

引用

3rd ieee Global conference for Advancement in Technology, GCAT 2022

作者： Dharavath, Krishna Bhukya, Shankar Vardhaman College of Engineering Shamshabad Hyderabad India Cvr College of Engineering Ibrahimpatnam Hyderabad India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

Image compression is an important issue in image information processing domain. The size of the image data is a crucial factor while processing image. It can affect the architecture of the design or memory size required, time required to process huge data and may increase complexity in processing. Therefore it is necessary to reduce the image data size by reducing the redundancies in the image data while retaining its quality same. In this paper we propose a new approach to compress an image while retaining all its features. Our approach shows superior performance over other compression techniques already available in the market. Our approach gives a compression ratio of 185.9 and a peak signal to noise ratio equal to 80.12. © 2022 ieee.

关键词： Image compression

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Convolutional Neural Network Accelerator for compression Based on Simon k-means

Convolutional Neural Network Accelerator for Compression Bas...

引用

ieee International conference on Fuzzy Systems (FUZZ-ieee) / ieee World Congress on Computational Intelligence (ieee WCCI) / International Joint conference on Neural Networks (IJCNN) / ieee Congress on Evolutionary Computation (ieee CEC)

作者： Wei, Mingjie Zhao, Yunping Lu, Jianzhuang Chen, Xiaowen Li, Chen Li, Zerun Natl Univ Def Technol Coll Comp Changsha Peoples R China

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Convolutional Neural Networks (CNN) are popular models widely used in image classification, target recognition, and other fields. FPGA-based accelerators for CNN are a standard method in recent years to reduce CNN's inference time and energy efficiency. However, the limitations of on-chip storage space and computing resources introduce deep compression. Contrary to most compression algorithms that pay no attention to the underlying hardware acceleration strategy and hardware-only accelerators, this paper introduces a novel model compression scheme with software and hardware collaboration for accelerating inference. First, we propose a pre-processing algorithm named Simon k-means based on clustering to quantify trained weight to speed up inference. Next, we propose a new encoding method for the quantized weight, significantly reducing the model's storage size. Finally, we give the architecture design of the accelerator using the quantized weight to accelerate the convolution. We have evaluated many popular CNNs in image classification tasks on various data sets. Experiments show that the number of multiply-accumulate operations on the convolutional layer can be reduced 66.6% with a slight loss of precision.

关键词： Convolutional neural network(CNN) deep learning k-means model compression weight quantization

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Impact of Intra-Refresh on Error Resilience and compression Efficiency in MV-HEVC 8

Impact of Intra-Refresh on Error Resilience and Compression ...

引用

8th ieee International conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Belbel, Amel Bekhouch, Amara Doghmane, Noureddine University Badji Mokhtar of Annaba Department of Electronics Annaba Algeria University of Souk Ahras Department of Computer Science Algeria

ISBN: (纸本)9798350309249

The latest multi-view video coding standard, MV-HEVC, efficiently represents multi-view video sequences, reducing the required network bitrate, and incorporates error resilience tools to maintain video quality even under adverse network conditions. This paper investigates the impact of random error propagation in the MV-HEVC Network Abstraction Layer (NAL) and presents a comprehensive analysis of MV-HEVC bitstream sensitivity under error-prone networks. The study identifies the most sensitive data within the MV-HEVC bitstream and proposes an intra-refresh transcoding scheme to enhance the robustness of the MV-HEVC bitstream transmission. Experimental results ensured better video quality under adverse network conditions with an improvement of 22 dB in the perceptual quality of received multi-view video with minimal performance overhead. © 2024 ieee.

关键词： Video signal processing

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SDT compression Algorithm Based on Approximate Query of Sensor Time Series data 5

SDT Compression Algorithm Based on Approximate Query of Sens...

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5th ieee International conference on Information Systems and Computer Aided Education, ICISCAE 2022

作者： Wang, Rubo School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing China

ISBN: (数字)9781665481229

ISBN: (纸本)9781665481229

In order to remove the large amount of redundant sensor timing data existing in industrial systems, improve the storage efficiency of storage media and the query efficiency of data, this paper proposes an AQ-SDT algorithm based on SDT algorithm, which provides a feasible way to approximate the query sensor time-series data, and ensures the credibility of the compressed data by dynamic search for the compression accuracy and linear regression fitting of the compressed line segments, which improves the compression rate and reduces the compression error. © 2022 ieee.

关键词： Storage efficiency

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Critical Understanding Performance of Huffman and Lempel Zip to Pattern Audio data 16-bit 6

Critical Understanding Performance of Huffman and Lempel Zip...

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6th International conference on Computer and Informatics Engineering, IC2IE 2023

作者： Hidayat, Tonny Nurcholis, Muhammad Tofa Nugroho, Agung Santoso, Joko Dwi Universitas Amikom Yogyakarta Faculty of Computer Sciences Yogyakarta Indonesia

ISBN: (纸本)9798350345162

This paper provides different perspectives on the Huffman and Lempel Ziv algorithms performance in terms of lossless compression. This performance will be tested using 16-bit audio data, which in related research is still not as much analysis as in this paper. Huffman and Lampel Zip are still included in entropy coding with a linear estimation combining system. Each method has advantages and disadvantages depending on the type of data being compressed, and until now no one has claimed which algorithm is the most effective and efficient in compressing data. As a result, this study attempts to assess size of the file, proportion, velocity, compression factor, and waveform properties. In the final results, it is known that Huffman is fast in compression with a ratio of 18.0051%, while Lempel Zip is 12.3554%. From the results of the final analysis, it was found that there were many differences in performance perceptions from related previous studies. © 2023 ieee.

关键词： 16-bit huffman lempel Ziv lossless

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