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IEEE Conference on Communications and Network Security (CNS)

作者： Senigagliesi, Linda Baldi, Marco Gambi, Ennio Univ Politecn Marche Dept Informat Engn I-60131 Ancona Italy

ISBN: (纸本)9781728147604

Wireless communications employing multi-carrier transmissions, like orthogonal frequency division multiplexing (OFDM) or single-carrier frequency division multiple access (SC-FDMA) may involve the use of a large number of subcarriers. In Internet of Things (IoT) contexts, however, the use of such technologies implies the fast management of large amounts of samples on devices with limited memory and computational resources. The adoption of physical layer authentication protocols in IoT may suffer from this fact, especially when they exploit machine learning algorithms yielding a significant computational burden. For instance, the complexity of Nearest Neighbor classifiers strictly depends on the training set dimension, which is directly proportional to the number of used subcarriers. In order to deal with this issue, we start from a naive approach based on random sampling of the input data to extract features, and then consider more advanced data dimension reduction algorithms, such as Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE). We show that PCA is able to guarantee the best trade-off between authentication performance and complexity, while the application of t-SNE is effective when one wants to reduce data to a very small number of features.

关键词： authentication compression algorithms machine learning physical layer security wireless networks

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An Efficient Regular Expressions compression Algorithm From A New Perspective

An Efficient Regular Expressions Compression Algorithm From ...

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IEEE INFOCOM Conference

作者： Liu, Tingwen Yang, Yifu Liu, Yanbing Sun, Yong Guo, Li Institute of Computing Technology Chinese Academy of Sciences 100190 China Graduate University Chinese Academy of Sciences Beijing 100039 China National Engineering Laboratory for Information Security Technologies China

ISBN: (纸本)9781424499212

Deep packet inspection plays a increasingly important role in network security devices and applications, which use more regular expressions to depict patterns. DFA engine is usually used as a classical representation for regular expressions to perform pattern matching, because it only need O(1) time to process one input character. However, DFAs of regular expression sets require large amount of memory, which limits the practical application of regular expressions in high-speed networks. Some compression algorithms have been proposed to address this issue in recent literatures. In this paper, we reconsider this problem from a new perspective, namely observing the characteristic of transition distribution inside each state, which is different from previous algorithms that observe transition characteristic among states. Furthermore, we introduce a new compression algorithm which can reduce 95% memory usage of DFA stably without significant impact on matching speed. Moreover, our work is orthogonal to previous compression algorithms, such as D2FA, delta FA. Our experiment results show that applying our work to them will have several times memory reduction, and matching speed of up to dozens of times comparing with original delta FA in software implementation.

关键词： Arrays Clustering algorithms compression algorithms Doped fiber amplifiers Memory management Pattern matching Sparse matrices

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Code compression using variable-to-fixed coding based on arithmetic coding

Code compression using variable-to-fixed coding based on ari...

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Data compression Conference 2003 (DCC2003)

作者： Xie, Y Wolf, W Lekatsas, H Princeton Univ Dept Elect Engn Princeton NJ 08540 USA

ISBN: (纸本)0769518966

Embedded computing systems are space and cost sensitive;memory is one of the most restricted resources, posing serious constraints on program size. Code compression, which is a special case of data compression where the input source is machine instructions, has been proposed as a solution to this problem. Previous work in code compression has focused on either fixed-to-variable coding or dictionary-based algorithms. We propose code compression schemes that use variable-to-fixed (V2F) length coding, based on arithmetic coding. Experiments show that the compression ratio using memoryless V2F coding for the TMS320C6x processor is on average 82.5% (defined as the ratio of the compressed over the uncompressed program) and decompression can be parallelized A Markov-based V2F coding based on arithmetic coding, can achieve an average compression ratio 72% for TMS320C6x, while decompression cannot be parallelized. Furthermore, our experiments have shown that arithmetic coding based V2F coding has similar compression performance with Tunstall coding. Finally, we present a power reduction scheme for the instruction bus using our V2F coding scheme.

关键词： Arithmetic compression algorithms Costs Data compression Dictionaries Embedded computing Embedded system Huffman coding Process design VLIW

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A 16-channel Real-time Adaptive Neural Signal compression Engine in 22nm FDSOI 21

A 16-channel Real-time Adaptive Neural Signal Compression En...

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21st IEEE Interregional NEWCAS Conference (NEWCAS)

作者： Guo, Liyuan Zeinolabedin, Seyed Mohammad Ali Schueffny, Franz Marcus Weisse, Annika Scholze, Stefan George, Richard Partzsch, Johannes Mayr, Christian Tech Univ Dresden Chair Highly Parallel VLSI Syst & Neuromicroelect Dresden Germany

ISBN: (纸本)9798350300246

The real-time multi-channel intracranial recording of neural signals is required in both neuroscientific research and clinical practice. Due to the limited power budget and the increasing number of recording channels, a compression engine is highly recommended. This paper proposes a 16-channel realtime adaptive compression engine (ACE) exploiting neural signal properties. It is able to switch between lossless and near-lossless compression modes. In near-lossless mode, it compresses the spike region lossless and discards the rest. The achieved space-saving ratio (SSR) is on average about 62.5% and 91% for lossless and near-lossless modes, respectively. It can save about 78.5% of the power consumption (near-lossless compression) compared to the transmission of raw neural signals. The 16-channel ACE is implemented in 22nm FDSOI technology and consumes 230.0 mu W dynamic- and 55.49 mu W leakage-power at 5 MHz.

关键词： Multi-channel neural signal compression compression algorithms adaptive coding digital signal processors low-power design

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Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy compression 24

Accelerating Communication in Deep Learning Recommendation M...

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2024 International Conference for High Performance Computing, Networking, Storage and Analysis

作者： Feng, Hao Zhang, Boyuan Ye, Fanjiang Si, Min Chu, Ching-Hsiang Tian, Jiannan Yin, Chunxing Deng, Stunner Hao, Yuchen Balaji, Pavan Geng, Tong Tao, Dingwen Indiana Univ Bloomington IN USA Meta Menlo Pk CA USA Univ Rochester Rochester NY USA Chinese Acad Sci Inst Comp Technol SKLP Beijing Peoples R China

ISBN: (数字)9798350352917

ISBN: (纸本)9798350352924;9798350352917

DLRM is a state-of-the-art recommendation system model that has gained widespread adoption across various industry applications. The large size of DLRM models, however, necessitates the use of multiple devices/GPUs for efficient training. A significant bottleneck in this process is the time-consuming all-to-all communication required to collect embedding data from all devices. To mitigate this, we introduce a method that employs error-bounded lossy compression to reduce the communication data size and accelerate DLRM training. We develop a novel error-bounded lossy compression algorithm, informed by an in-depth analysis of embedding data features, to achieve high compression ratios. Moreover, we introduce a dual-level adaptive strategy for error-bound adjustment, spanning both table-wise and iteration-wise aspects, to balance the compression benefits with the potential impacts on accuracy. We further optimize our compressor for PyTorch tensors on GPUs, minimizing compression overhead. Evaluation shows that our method achieves a 1.38x training speedup with a minimal accuracy impact.

关键词： Training Deep learning Analytical models Accuracy Tensors High performance computing Computational modeling Industry applications Recommender systems compression algorithms

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Deployment of IPv6 robust header compression profiles 1 and 2 4

Deployment of IPv6 robust header compression profiles 1 and ...

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4th Mexican International Conference on Computer Science (ENC 2003)

作者： Minaburo, A Toutain, L Nuaymi, L ENST BRETAGNE Rennes France

ISBN: (纸本)0769519156

The use of interactive services in a low bandwidth link leads to a weak performance because of the large size of encapsulations. The performance could be increased using a header compression algorithm. The IETF ROHC working group has defined a new header compression algorithm used in low bandwidth links with high-level errors. This paper gives a description of the proposed infrastructure for ROHC and the differences between the profile I and profile 2 based profiles that are used. The results are based in our IPv6 ROHC implementation.

关键词： Bandwidth Bit error rate compression algorithms Encapsulation Land mobile radio cellular systems Radio link Redundancy Robustness TCPIP Transport protocols

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Progressive geometry compression 00

Progressive geometry compression

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Computer Graphics Annual Conference

作者： Khodakovsky, A Schröder, P Sweldens, W CALTECH Pasadena CA 91125 USA

ISBN: (纸本)1581132085

We propose a new progressive compression scheme for arbitrary topology, highly detailed and densely sampled meshes arising from geometry scanning. We observe that meshes consist of three distinct components: geometry, parameter, and connectivity information. The latter two do not contribute to the reduction of error in a compression setting. Using semi-regular meshes, parameter and connectivity information can be virtually eliminated. Coupled with semi-regular wavelet transforms, zerotree coding, and subdivision based reconstruction we see improvements in error by a factor four (12dB) compared to other progressive coding schemes. CR Categories and Subject Descriptors: I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling - hierarchy and geometric transformations;G.1.2 [Numerical Analysis]: Approximation - approximation of surfaces and contours, wavelets and fractals;I.4.2 [Image Processing and Computer Vision]: compression (Coding) - Approximate methods.

关键词： compression algorithms signal processing wavelets subdivision surfaces semi-regular meshes zerotree coding hierarchical representations

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SortComp (Sort-and-Compress) - Towards a Universal Lossless compression Scheme for Matrix and Tabular Data

SortComp (Sort-and-Compress) - Towards a Universal Lossless ...

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Data compression Conference (DCC)

作者： Cheng, Xizhe Lin, Sian-Jheng Sun, Jie Huawei Technol Co Ltd Hong Kong R&D Ctr Hong Kong Peoples R China

ISBN: (纸本)9781665478939

A universal scheme is proposed for the lossless compression of two-dimensional tables and matrices. Instead of standard row- or column-based compression, we propose to sort each column first and record both the sorted table and the corresponding permutation table of the sorting permutations. These two tables are then separately compressed. In this new scheme, both intra- and inter-column correlations can be efficiently captured, giving rise to improved compression ratio in particular when both column-wise and row-wise dependencies cooccur. This scheme reduces the problem of the compression of an arbitrary two-dimensional table to that of a 'permutation table' together with a 'sorted table', where the former is only dependent on the table dimension and the latter can be effectively compressed column-by-column using predictive methods. Based on this scheme, a new algorithm is proposed, SortComp (sort-and-compress). For correlated columns, we give an estimation of the asymptotic bit rate of the algorithm and compare it to column-oriented compression schemes. Numerical experiments on real-life csv datasets validate the advantages of SortComp compared to existing row- and column-oriented compression algorithms.

关键词： Correlation Bit rate Estimation Data compression Prediction algorithms Standards compression algorithms

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Polylog Space compression Is Incomparable with Lempel-Ziv and Pushdown compression

Polylog Space Compression Is Incomparable with Lempel-Ziv an...

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35th Conference on Current Trends in Theory and Practice of Computer Science

作者： Mayordomo, Elvira Moser, Philippe Univ Zaragoza Dept Informat & Ingn Sistemas Maria de Luna 1 Zaragoza 50018 Spain Natl Univ Ireland Maynooth Dept Comp Sci Maynooth Kildare Ireland

This paper considers online compression algorithms that use at most polylogarithmic space (plogon). These algorithms correspond to compressors in the data stream model. We study the performance attained by these algor... 详细信息

ISBN: (纸本)9783540958901

关键词： compression algorithms plogon computational complexity data stream algorithms Lempel-Ziv algorithm pushdown compression

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A DOUBLE-ADAPTIVE FILE compression ALGORITHM

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IEEE TRANSACTIONS ON COMMUNICATIONS 1983年第11期31卷 1253-1255页

作者： LANGDON, GG RISSANEN, JJ IBM Corporation San Jose CA USA

We describe a one-pass compression scheme which presumes no statistical properties of the data being compressed. The model structure adaptively selects a subset of first-order Markov contexts, based on an estimate of the candidate context's popularity. The probability distributions for the unselected (lumped) first-order contexts are made the same, reducing cost over a full first-order Markov model. Symbol repetitions are handled in special secondorder Markov contexts. The statistics for each symbol are adaptively determined by an extension of earlier work.

关键词： compression algorithms Pulse shaping methods Symmetric matrices Shape Context modeling Image coding Piecewise linear techniques Statistics Lapping Constraint optimization

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