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

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DenseStream: A Novel data Representation for Gradient Sparsification in Distributed Synchronous SGD Algorithms

DenseStream: A Novel Data Representation for Gradient Sparsi...

引用

International Joint conference on Neural Networks (IJCNN)

作者： Li, Guangyao Liao, Mingxue Chao, Yongyue Lv, Pin Chinese Acad Sci Inst Automat Beijing Peoples R China

ISBN: (纸本)9781665488679

Distributed training is widely used in training large-scale deep learning model, and data parallelism is one of the dominant algorithms. data-parallel training has additional communication overhead, which greatly affects the training at low bandwidth. Gradient sparsification is a promising technique to reduce the communication volume, which keeps a small number of important gradient values and sets the rest to zero. However, the communication of sparsified gradients suffer from scalability issues for (1) the communication volume of the AllGather algorithm, which is commonly used to accumulate sparse gradients, increases linearly with the number of nodes, and (2) sparse local gradients may return dense due to gradient accumulation. These issues hinder the application of gradient sparsification. We observe that sparse gradient value distribution has great locality, and therefore we propose DenseStream, a novel data representation for sparse gradients in data-parallel training to alleviate the issues. DenseStream integrates an efficient sparse AllReduce algorithm with the synchronous SGD (S-SGD). Evaluations are conducted by real-world applications. Experimental results show that DenseStream achieves better compression ratio at higher densities and can represent sparse vectors with a wider range of densities. Compared with dense AllReduce, our method is more scalable and achieves 3.1-12.1x improvement.

关键词： deep learning AllReduce gradient sparsification data representation

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SILGAN: Generative Adversarial Networks for Multimedia data compression in Solar Insecticidal Lamps Internet of Things 49

SILGAN: Generative Adversarial Networks for Multimedia Data ...

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49th Annual conference of the ieee Industrial Electronics Society, IECON 2023

作者： Chen, Mingying Liu, Ye Shu, Lei Li, Kailiang Yang, Xing Yang, Fan Anhui Higher Education Institutes Key Laboratory of Architectural Acoustic Environment Hefei230601 China College of Artificial Intelligence Nanjing Agricultural University Nangjing China School of Engineering University of Lincoln United Kingdom School of Mathematics and Statistics Jiangsu Normal University Xuzhou221116 China

ISBN: (纸本)9798350331820

This paper presents a low overhead multimedia data compression method for Solar Insecticidal Lamps Internet of Things (SIL-IoTs), achieving efficient audio data transmission. First, the audio and video data generated by working solar insecticidal lamps are collected to form an original dataset in the solar insecticidal lamps applications. Then, we propose SILGAN, a generative adversarial network to compress multimedia audio data in the SIL-IoTs. Specifically, a depth-wise separable convolution is adopted to reduce the computational resources for training networks and running programs. Moreover, the network parameters are optimized so that obtaining a better neural network model with stable operation on the nodes of the SIL-IoTs. Finally, experimental evaluation is conducted, showing the effectiveness of the proposed SILGAN approach. © 2023 ieee.

关键词： data compression

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Deep Learning-Enabled Squirrel Detecting Prototype Development and Processing Analysis 20

Deep Learning-Enabled Squirrel Detecting Prototype Developme...

引用

20th ieee India Council International conference, INDICON 2023

作者： Sardana, Tushar Bitragunta, Sainath Birla Institute of Technology and Science Pilani Campus Department of Electrical and Electronics Engineering Rajasthan Pilani333031 India

ISBN: (纸本)9798350305593

In this paper, the authors present the development of an object detection prototype aimed at detecting living objects in front of vehicles. The proposed and implemented prototype focuses on detecting squirrels and alerting the driver to avoid collision. This can be further extended to other small animals. The prototype uses transfer learning to leverage pre-existing knowledge from a larger, more robust model and apply it to a smaller, more specific task. The model is able to reach an accuracy of greater than 95% on color images with the help of this approach. The detection models are designed to be implemented on small, low-power devices integrated into vehicle electronics that are useful for object detection. The paper further focuses on a detailed comparison of different compression techniques to reduce the size of the data required for classification, enabling the system to run more efficiently and effectively. Further, the drop in accuracy and the total inference time are compared for different compression techniques. The study uniquely compares the different image compression and inference processing techniques which can be used as a base for future studies. © 2023 ieee.

关键词： embedded systems image compression Object detection prototyping squirrel detection transfer learning

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Adaptive Human-Centric Video compression for Humans and Machines

Adaptive Human-Centric Video Compression for Humans and Mach...

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

作者： Jiang, Wei Choi, Hyomin Racapé, Fabien InterDigital Inc. Emerging Technologies Lab United States

ISBN: (纸本)9798350302493

We propose a novel framework to compress human-centric videos for both human viewing and machine analytics. Our system uses three coding branches to combine the power of generic face-prior learning with data-dependent detail recovery. The generic branch embeds faces into a discrete code space described by a learned high-quality (HQ) codebook, to reconstruct an HQ baseline face. The domain-adaptive branch adjusts reconstruction to fit the current data domain by adding domain-specific information through a supplementary codebook. The task-adaptive branch derives assistive details from a low-quality (LQ) input to help machine analytics on the restored face. Adaptive weights are introduced to balance the use of domain-adaptive and task-adaptive features in reconstruction, driving trade-offs among criteria including perceptual quality, fidelity, bitrate, and task accuracy. Moreover, the proposed online learning mechanism automatically adjusts the adaptive weights according to the actual compression needs. By sharing the main generic branch, our framework can extend to multiple data domains and multiple tasks more flexibly compared to conventional coding schemes. Our experiments demonstrate that at very low bitrates we can restore faces with high perceptual quality for human viewing while maintaining high recognition accuracy for machine use. © 2023 ieee.

关键词： Image compression

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Segmentation-based Initialization for Steered Mixture of Experts

Segmentation-based Initialization for Steered Mixture of Exp...

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2023 ieee International conference on Visual Communications and Image Processing, VCIP 2023

作者： Li, Yi-Hsin Sjostrom, Marten Knorr, Sebastian Sikora, Thomas Technical University of Berlin Germany Mid Sweden University Sweden Ernst-Abbe University of Applied Sciences Jena Germany

ISBN: (纸本)9798350359855

The Steered-Mixture-of-Experts (SMoE) model is an edge-Aware kernel representation that has successfully been explored for the compression of images, video, and higher-dimensional data such as light fields. The present work aims to leverage the potential for enhanced compression gains through efficient kernel reduction. We propose a fast segmentation-based strategy to identify a sufficient number of kernels for representing an image and giving initial kernel parametrization. The strategy implies both reduced memory footprint and reduced computational complexity for the subsequent parameter optimization, resulting in an overall faster processing time. Fewer kernels, when combined with the inherent sparsity of the SMoEs, further enhance the overall compression performance. Empirical evaluations demonstrate a gain of 0.3-1.0 dB in PSNR for a constant number of kernels, and the use of 23 % less kernels and 25 % less time for constant PSNR. The results highlight the feasibility and practicality of the approach, positioning it as a valuable solution for various image-related applications, including image compression. © 2023 ieee.

关键词： Image compression

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Point Cloud compression for Efficient data Broadcasting: A Performance Comparison

Point Cloud Compression for Efficient Data Broadcasting: A P...

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ieee Wireless Communications and Networking conference (ieee WCNC)

作者： Nardo, Francesco Peressoni, Davide Testolina, Paolo Giordani, Marco Zanella, Andrea Univ Padua Dept Informat Engn Padua Italy

ISBN: (纸本)9781665442664

The worldwide commercialization of fifth generation (5G) wireless networks and the exciting possibilities offered by connected and autonomous vehicles (CAVs) are pushing toward the deployment of heterogeneous sensors for tracking dynamic objects in the automotive environment. Among them, Light Detection and Ranging (LiDAR) sensors are witnessing a surge in popularity as their application to vehicular networks seem particularly promising. LiDARs can indeed produce a three-dimensional (3D) mapping of the surrounding environment, which can be used for object detection, recognition, and topography. These data are encoded as a point cloud which, when transmitted, may pose significant challenges to the communication systems as it can easily congest the wireless channel. Along these lines, this paper investigates how to compress point clouds in a fast and efficient way. Both 2D- and a 3D-oriented approaches are considered, and the performance of the corresponding techniques is analyzed in terms of (de)compression time, efficiency, and quality of the decompressed frame compared to the original. We demonstrate that, thanks to the matrix form in which LiDAR frames are saved, compression methods that are typically applied for 2D images give equivalent results, if not better, than those specifically designed for 3D point clouds.

关键词： LiDAR point cloud compression autonomous driving data broadcasting performance comparison

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Signal compression with waves

Signal compression with waves

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2024 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2024

作者： Sounas, Dimitrios L. Wayne State University DetroitMI48202 United States

ISBN: (纸本)9781946815194

Signal compression is one of the most important operations in systems that are used to store or transmit information, since it allows efficient use of the available bandwidth or storage capacity. A typical compression system, such as those that are used in the JPEG standard, involves a linear matrix operation, like the discrete wavelet transform, followed by a series of nonlinear operations, such as quantization of the signal and application of a code. As in many other instances of signal processing systems, the matrix operation is the most computationally demanding one since its complexity scales nonlinearly with the size of the input data. Therefore, compression systems would benefit significantly if the involved matrix operation could be implemented in the analog domain and in a parallel manner without the use of active components. © 2024 ieee. All rights reserved.

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VQ-VAE Empowered Wireless Communication for Joint Source-Channel Coding and Beyond

VQ-VAE Empowered Wireless Communication for Joint Source-Cha...

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ieee conference on Global Communications (ieee GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Nemati, Mahyar Park, Jihong Choi, Jinho Deakin Univ Sch Informat Technol Geelong Vic Australia

ISBN: (纸本)9798350310900

Vector Quantized Variational Autoencoder (VQ-VAE) has shown promise in representing diverse and complex data distributions in deep learning, making it a potential solution for various applications including wireless communications. In this paper, we propose a joint source-channel coding scheme based on VQ-VAE for point-to-point wireless communication. Our approach leverages the dependence of the encoder and decoder on a given dataset and channel conditions to develop efficient encoding and decoding schemes, leading to improved reliability and efficiency even in the presence of noisy wireless channels. We demonstrate the effectiveness of our proposed approach through extensive simulations in handling realistic wireless communication scenarios. In addition, we discuss potential connections to semantic communication and highlight the secure and energy-efficient nature of our approach.

关键词： Joint Source and Channel Coding Lossy compression Noisy Channels VQ-VAE

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Application of Various Novel Computer Image Processing (CIP) Algorithms in Digital Media Art Images

Application of Various Novel Computer Image Processing (CIP)...

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2025 International conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Du, Xian Shanghai Institute of Visual Arts Shanghai201620 China

ISBN: (纸本)9798331509828

With the continuous development of digital image processing algorithms, its application scenarios have been integrated from the simple research of a single image and a single algorithm to a multi-algorithm fusion analysis paradigm. Therefore, this study proposes a digital media art image analysis framework that combines multiple computer image processing (CIP) algorithms. This breakthrough covers three core architectures: image edge extraction, image style transfer, and image compression. In the image edge extraction module, this study designed an improved edge detection algorithm based on de-noising auto-encoder. This algorithm improves the accuracy of edge detection through multi-directional feature extraction and (I, O)-fuzzy rough set optimization, while maintaining global stability. In the image style transfer module, this study proposed a dual-module network. The network includes a texture translation network and a perceptual loss network, and achieves cross-domain style transfer through a fusion model. At the same time, the algorithm optimizes the perceptual loss function to enhance semantic matching capabilities. In the image compression module, this study uses chaotic systems to construct an optimized measurement matrix. Specifically, through a distributed data parallel training framework, a compression method based on the LPAC gradient sparse algorithm is constructed. By testing the three modules separately, the experimental results confirm the effectiveness of the proposed algorithm. © 2025 ieee.

关键词： Image compression

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Exploring the Impacts of Software Cache Configuration for In-line Compressed Arrays

Exploring the Impacts of Software Cache Configuration for In...

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ieee High Performance Extreme Computing Virtual conference (HPEC)

作者： Ranjan, Sansriti Fulp, Dakota Calhoun, Jon C. Clemson Univ Holcombe Dept Elect & Comp Engn Clemson SC 29634 USA

ISBN: (数字)9781665497862

ISBN: (纸本)9781665497862

In order to compute on or analyze large data sets, applications need access to large amounts of memory. To increase the amount of physical memory requires costly hardware upgrades. Compressing large arrays stored in an application's memory does not require hardware upgrades, while enabling the appearance of more physical memory. In-line compressed arrays compress and decompress data needed by the application as it moves in and out of it's working set that resides in main memory. Naive compressed arrays require a compression or decompression operation for each store or load, respectively, which significantly hurts performance. Caching decompressed values in a software managed cache limits the number of compression/decompression operations, improving performance. The structure of the software cache impacts the performance of the application. In this paper, we build and utilize a compression cache simulator to analyze and simulate various cache configurations for an application. Our simulator is able to leverage and model the multidimensional nature of high-performance computing (HPC) data and compressors. We evaluate both direct-mapped and set-associative caches on five HPC kernels. Finally, we construct a performance model to explore runtime impacts of cache configurations. Results show that cache policy tuning by increasing the block size, associativity and cache size improves the hit rate significantly for all applications. Incorporating dimensionality further improves locality and hit rate, achieving speedup in the performance of an application by up to 28.25%.

关键词： in-line compression compressed arrays lossy compression big-data software cache cache configuration

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