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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Hanes Oliveira Gaetan Delavignette Luke Harmon Bhagiradh Kantheti Algemetric Colorado Springs Colorado USA

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

The large size of genome files and their current widespread use in many fields makes compression of these files very important for their efficient storage. However, many compression tools, e.g. gzip, require the entire file to be decompressed before any part can be accessed. With this problem in mind, we introduce a reference-free compression transform for genome files that support compressed pattern matching - namely, the capacity to search the compressed file for information contained in the original file. Our transform, Slot Machine, leverages the distribution of characters in multi-FASTA files to decrease their size by approximately 66% while allowing a user to search and decompress only the parts of the file needed at any given time. As an added bonus, the output of Slot Machine can be further compressed by pairing with existing compressors. In our experiments, we achieved compression-ratios of up to 5.25, or a compression rate of 80.96%. In that measure, our results improved upon NAF in every experiment and are comparable to MFCompress.

关键词： Genomics Transforms Search problems Compressors Information and communication technology Bioinformatics Pattern matching compression algorithms Convergence

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Extending the RISC-V Instruction Set for High Performance Data compression Hardware Acceleration

Extending the RISC-V Instruction Set for High Performance Da...

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International Conference on Application Specific Systems (ASAP), Architectures and Processors

作者： Junzhe Huang Qiang Dou Li Shen National University of Defense Technology Changsha China Phytium Technology Co. Ltd. Tianjin China

ISBN: (数字)9798350349634

ISBN: (纸本)9798350349641

With the advent of the big data era, the exponen-tially growing data processing requirements pose a huge challenge to data compression. Existing FPGA hardware acceleration schemes have many problems and a new hardware acceleration scheme needs to be explored. There are a large number of parallelizable loops in the data compression algorithm, so they can be accelerated by vectorization. In this paper, we improve RISC- V Vector Extension (RVV) for the data compression. We analyze five common compression algorithms and design a class of vector adjacency instructions for vectorization acceleration for hotspot loops in compression algorithms that cannot use RVV vectorization. We design a decoupled vector arithmetic unit for these instructions that is able to complete computations with data-dependent loops in a non-blocking way. The open source vector processor Ara is used to implement our ideas and is synthesized and implemented on the Alveo U50. The results show that our work brings a maximum speedup of 13.24x in cycle count.

关键词： Data compression Systems architecture Vectors Timing Vector processors compression algorithms Hardware acceleration

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Generalized compression and compressive search of large datasets

Generalized compression and compressive search of large data...

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IEEE International Conference on Big Data

作者： Morgan E. Prior Thomas J. Howard Emily Light Najib Ishaq Noah M. Daniels Dept. of Computer Science Tufts University Boston MA USA Dept. of Computer Science and Statistics University of Rhode Island Kingston RI USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The Big Data explosion has necessitated the development of search algorithms that scale sub-linearly in time and memory. While compression algorithms and search algorithms do exist independently, few algorithms offer both, and those which do are domain-specific. We present panCAKES, a novel approach to compressive search, i.e., a way to perform k-NN and ρ-NN search on compressed data while only decompressing a small, relevant, portion of the data. panCAKES assumes the manifold hypothesis and leverages the low-dimensional structure of the data to compress and search it efficiently. panCAKES is generic over any distance function for which the distance between two points is proportional to the memory cost of storing an encoding of one in terms of the other. This property holds for many widely-used distance functions, e.g. string edit distances (Levenshtein, Needleman-Wunsch, etc.) and set dissimilarity measures (Jaccard, Dice, etc.). We benchmark panCAKES on a variety of datasets, including genomic, proteomic, and set data. We compare compression ratios to gzip, and search performance between the compressed and uncompressed versions of the same dataset. panCAKES achieves compression ratios close to those of gzip, while offering sub-linear time performance for k-NN and ρ-NN search. We conclude that panCAKES is an efficient, general-purpose algorithm for exact compressive search on large datasets that obey the manifold hypothesis. We provide an open-source implementation of panCAKES in the Rust programming language.

关键词： Manifolds Computer languages Costs Memory management Genomics Proteomics Big Data Explosions Encoding compression algorithms

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An Efficient compression Method for Collaborative 3D Mapping in Confined Space with Limited Resources

An Efficient Compression Method for Collaborative 3D Mapping...

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Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Zhengquan Piao Fuyong Feng Ruina Dang Zhenhao Wang Tianming Li Zhewei Qiu Di Wang China North Artificial Intelligence & Innovation Research Institute Beijing China Collective Intelligence & Collaboratioon Laboratory Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

LiDAR is a key sensor for 3D mapping, and as the measurement accuracy of the sensor continues to improve, the order of magnitude and the memory required to acquire 3D point cloud data are getting larger and larger. However, due to the limited computational resources of unmanned platforms for collaborative 3D mapping in confined space, the large memory occupancy of the point cloud data will bring challenges to the subsequent transmission, storage and collaboration. In order to save storage space and improve transmission and processing efficiency, this paper expresses the 3D point cloud based on the voxel octree structure. We use cropping to remove the irrelevant point cloud information first, and further optimize the compression algorithm of the voxel octree by detecting the point cloud resolution and then setting the voxel filter in partitions. Experimental results verify the proposed method compress the point cloud as much as possible while reducing the loss of key information.

关键词： Point cloud compression Three-dimensional displays Laser radar Octrees Memory management Collaboration Information filters Real-time systems Standards compression algorithms

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Transformer-based Lossless compression of Aurora Spectral Images: A Spatial-Temporal-Spectral Joint Approach

Transformer-based Lossless Compression of Aurora Spectral Im...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Xinlong Cao Jiaojiao Li Yuanqing Li Jiapeng Yan Zhenxing Zheng School of Communications and Information Engineering & School of Artificial Intelligence Xi'an University of Posts & Telecommunications Xi'an China Xi’an Key Laboratory of Image Processing Technology and Applications for Public Security Xi'an China International Joint Research Center for Wireless Communication and Information Processing Technology of Shaanxi Province Xi'an China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Aurora spectral image lossless compression has seen significant advancements in recent years. However, most compression algorithms are based on traditional image compression techniques, focusing solely on spectral and spatial correlations without considering temporal correlations. To further enhance compression performance, this paper proposes a Transformer-based point-by-point prediction algorithm for auroral spectral images, which simultaneously uses spatial, spectral and temporal contexts for prediction. Our prediction network consists of two parts: an encoder and a decoder. The encoder is composed of the encoding unit of the original Transformer and is used to extract features from spatial, spectral and temporal contexts. The decoder consists of fully connected layers and is used for prediction. Experimental results show that the average bitrate of this method is reduced by 0.179 bpp compared with the JPEG2000 algorithm, and the average bitrate is reduced by 0.054 bpp compared with the online DPCM algorithm.

关键词： Ion radiation effects Image coding Correlation Signal processing algorithms Transformers Prediction algorithms Feature extraction Decoding Magnetosphere compression algorithms

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A Sparsity Based Adaptive compression Algorithm for Model compression

A Sparsity Based Adaptive Compression Algorithm for Model Co...

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Electrical Engineering and Control Technologies (CEECT), Conference on

作者： Yifu Lv Yueli Hu Department of Electrical Engineering School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798331528096

ISBN: (纸本)9798331528102

With the development of convolutional neural networks, the scale of activation data has increased dramatically, posing significant challenges to model storage and computation. The insight indicates that the sparsity of activations is significantly different across network layers, due to the differences in activation functions. Most existing studies are limited to applying a single compression algorithm to the entire model. This work proposes an adaptive compression method based on sparsity. The optimization strategy includes an adaptive selector that chooses the appropriate compression algorithm based on the sparsity level of each layer, thereby achieving high compression ratios. In comparison to three state-of-the-art compression methods, the proposed algorithm achieves superior compression performance on four mainstream CNN models at different data accuracies.

关键词： YOLO Electrical engineering Adaptation models Adaptive systems Computational modeling Neural network compression Data models Convolutional neural networks compression algorithms Optimization

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SLAP-CC: Set-Level Adaptive Prefetching for Compressed Caches 40

SLAP-CC: Set-Level Adaptive Prefetching for Compressed Cache...

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IEEE 40th International Conference on Computer Design (ICCD)

作者： AlBarakat, Laith M. Gratz, Paul, V Jimenez, Daniel A. Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA Texas A&M Univ Dept Comp Sci & Engn College Stn TX USA

ISBN: (数字)9781665461863

ISBN: (纸本)9781665461863

Data prefetching and cache compression are well-studied techniques to reduce the impact of memory latency. Data prefetching predicts future memory accesses and prefills the cache with the corresponding memory blocks in advance of explicit demands. Cache compression tries to increase the effective capacity of the cache with minimum area overhead. As we show, however, naively integrating the two techniques does not yield additive gains. We show that the extra ways per set that cache compression provides generally produce fewer hits than the ways in the baseline uncompressed cache. Hence, prefetching more aggressively to those sets with compression-added ways and more conservatively to those sets with less compression provides substantial benefit. In this paper we present set-level adaptive prefetching for compressed caches (SLAP-CC), a compressibility aware prefetching technique that adapts prefetch aggressiveness to the workload compressibility to maximize prefetch coverage. SLAP-CC dynamically adjusts the prefetch confidence threshold on a per-set basis, based on the set's compression. SLAP-CC achieves a geometric mean speedup of 18.0% over a baseline system with compressed cache and no prefetching. Further, SLAP-CC significantly outperforms existing state of the art L2 prefetchers, SPP [1] and Best Offset [2] when combined with cache compression by an average of 7% and up to 25% on some workloads.

关键词： Additives Filtering Prefetching Data compression compression algorithms

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SnappyR: A New High-Speed Lossless Data compression Algorithm

SnappyR: A New High-Speed Lossless Data Compression Algorith...

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Data compression Conference (DCC)

作者： Rui Chen Lihao Xu Mathworks Inc Natick MA Wayne State University Detroit MI

We propose a high-speed lossless data compression algorithm, named SnappyR. Improved upon Snappy, we design new structures of the literal and the match tokens to achieve better compression ratio than Snappy. Numerous benchmarks are conducted on different sets of data corpus. The evaluations consistently show that SnappyR provides a better compression ratio comparing to Snappy, as well as LZ4, and better than LZO in most cases. Although a little slower than Snappy, the compression and decompression speeds of SnappyR are still much higher than entropy encoding based compression algorithms, such as ZSTD, deflate or Zlib. Thus, SnappyR can become another viable replacement or alternative to Snappy, LZ4 or LZO for computing and storage systems and applications, where high-speed lossless data compression is needed. More details about SnappyR algorithm design and performance evaluation can be found at [1].

关键词： Performance evaluation Data compression Benchmark testing Entropy Encoding compression algorithms

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A Comparison of On-Mote Lossy compression algorithms for Wireless Seismic Data Acquisition

A Comparison of On-Mote Lossy Compression Algorithms for Wir...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Marc J. Rubin Michael B. Wakin Tracy Camp Dept. of Electrical Engineering and Computer Science Colorado School of Mines Golden CO

In this article, we rigorously compare compressive sampling (CS) to four state of the art, on-mote, lossy compression algorithms (K-run-length encoding (KRLE), lightweight temporal compression (LTC), wavelet quantization thresholding and run-length encoding (WQTR), and a low-pass filtered fast Fourier transform (FFT)). Specifically, we first simulate lossy compression on two real-world seismic data sets, and we then evaluate algorithm performance using implementations on real hardware. In terms of compression rates, recovered signal error, power consumption, and classification accuracy of a seismic event detection task (on decompressed signals), results show that CS performs comparable to (and in many cases better than) the other algorithms evaluated. The main benefit to users is that CS, a lightweight and non-adaptive compression technique, can guarantee a desired level of compression performance (and thus, radio usage and power consumption) without subjugating recovered signal quality. Our contribution is a novel and rigorous comparison of five state of the art, on-mote, lossy compression algorithms in simulation on real-world data sets and implemented on hardware.

关键词： Accuracy compression algorithms Encoding Slabs Wavelet transforms Wireless sensor networks Wireless communication

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Performance Assessment of the CCSDS-123 Standard for Panchromatic Video compression on Space Missions

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷

作者： Barrios, Yubal Guerra, Raul Lopez, Sebastian Sarmiento, Roberto Univ Las Palmas de Gran Canaria ULPGC Inst Appl Microelect IUMA Las Palmas Gran Canaria 35017 Spain

The use of next-generation and high-resolution imaging sensors is gaining interest for space missions, because of their properties for identification and exploration purposes. It is expected that the demand for video sensors in the space industry will increase during the next years, mainly for monitoring and exploration missions. In this context, onboard video compression techniques emerge as mandatory, because memory resources are available on space missions and the downlink bandwidth with ground prevents for the transmission of raw video, especially when near real-time capabilities are required. The complexity of the current commercial video encoders supposes a challenge for their implementation on the hardware used on space missions, because of their high architectural complexity and computational requirements, deriving in an area overhead and an unacceptable power consumption. In this context, we propose the use of the CCSDS-123.0-B-2 standard, originally focused on the near-lossless on-board compression of multispectral and hyperspectral images, for compressing panchromatic video. The proposed solution presents low complexity, because it is specifically designed for working on space missions and also has the capability of compressing both 3-D images and panchromatic video by using a single processing core. Results demonstrate that this solution achieves high compression ratios, maintaining a considerable video quality after reconstruction on ground and achieving a fine-grain control of the losses introduced in the compression chain.

关键词： Image coding Space missions Standards Sensors Video sequences Video compression Hyperspectral imaging compression algorithms Consultative Committee for Space Data Systems (CCSDS) hyperspectral imaging on-board processing panchromatic video compression space missions

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