Efficient and Accuracy-Ensured Waveform Compression for Transient Circuit Simulation

This paper conducts a comparative analysis of the impact of unsparsing by data scaling on lossless data compression methods. The most commonly used data compression algorithms such as gzip, zlib, bzip2, and lzma are tested by their compression efficiency in unsparsed datasets by the performance metrics such as compression time, decompression time, and compression ratio for both the original and scaled datasets. Five different data scaling techniques are investigated to transform the original data into unsparsed data such as min-max, robust, absolute max, standardization, and normalization. Our research reveals relationships between data scaling methodologies and compression performance, with disparities in compression efficiency and computational complexity. Fur-thermore, we investigate the effects of scaling on compression ratio and provide results for improving the understanding of the factors that influence lossless data compression methods for non-sparse tabular data.

With the resolution enhancement techniques such as OPC (Optical Proximity Correction) and SRAF (Sub-Resolution Assist Features), the size of layout data have grown significantly. It is quite common now to find layout files that are tens of GBs in size. Unlike GDSII which can store data hierarchically, mask data formats such as MEBES are essentially flat and more voluminous. Moreover, polygonal data present in layout data files is fractured, thereby increasing the data volume before getting stored in MEBES data format. This results in huge MEBES files. As per the ITRS roadmap of 2005, for a 45nm half-pitch node that is expected to be in use by 2010, the mask data volume for a single layer is expected to reach up to 825 GB. Storing and transferring such large mask data are issues for which the mask industry needs solutions. Historically, MEBES is the most prevalent EB format in the industry. Moreover, in many Mask Data Preparation (MDP) flows, the MEBES format is being used as the de-facto standard for specifying the fractured EB data even though the final target EB machine might be different. In this paper we present techniques for lossless reversible compression of MEBES data, i.e., when the compressed file is decompressed, the generated uncompressed file matches the original MEBES file bit- by-bit. By applying these compression techniques a compression ratio of 5X to 15X can be obtained. In practice, compressing MEBES files is usually a one-time task, but decompression of compressed files is expected to be done multiple times as every time a compressed MEBES file needs processing, it has to be decompressed. MEBES is essentially an efficient data format and the geometries are stored compactly. As a result the compression/decompression techniques described in this paper are quite computation intensive in order to achieve higher compression ratio. This in turn leads to higher CPU time for compression/decompression compared to generic compressors such as gzip. However, as the format-specific compressors produce higher compression ratios, the disk I/O time for compression and decompression is expected to be less compared to the generic compressors such as gzip and gunzip. In spite of this, the format-specific decompressor is usually 3-5X times slower than the generic decompressor such as gunzip. Since decompression is expected to be done more frequently as compared to compression, speeding up decompression is highly desirable. We present a compression technique for MEBES data which enables multiple process threads to decompress the compressed MEBES data. With the multi-core multiprocessor machines becoming quite inexpensive and common, the multi-threaded decompression is expected to perform close to the disk I/O time on such machines. The paper details out the techniques, experimental results in terms of comparative compression ratios, compression and decompression speed using single threads and multiple threads. The possibility of getting higher compression ratios as well as higher or comparable decompression speed makes it more practical to use format specific reversible compression schemes rather than using generic compressors. Even though, the paper focuses on compression and decompression of MEBES, it can be easily extended to the compression of GDSII [1].

Time series compression encodes the information in a time-ordered sequence of data points into fewer bits, thereby reducing storage costs and possibly other costs. Compression methods are either general or XOR-based. General compression methods are time-consuming and are not suitable in streaming scenarios, while XOR-based methods are unable to consistently maintain high compression ratios. Further, existing methods compress the integer and decimal parts of floating-point values as a whole, thus disregarding the different characteristics of the two parts. We propose Camel , a new compression method for floating-point time series with the goal of advancing the compression ratios and efficiency achievable. Camel compresses the integer and decimal parts of the double-precision floating-point numbers in time series separately; and instead of performing XOR operations on values using their previous value, Camel identifies values that enable higher compression ratios. Camel also includes means of indexing compressed data, thereby making it possible to query compressed data efficiently. We report on an empirical study of Camel and 11 lossless and 6 lossy compression methods on 22 public datasets and three industrial datasets from AliCloud. The study offers evidence that Camel is capable of outperforming existing methods in terms of both compression ratio and efficiency and is capable of excellent compression performance on both time series and non-time series data.

The rapid growth of smart grid systems has resulted in an exponential increase in data volume. This has led to significant challenges for efficient storage and transmission, thereby necessitating the development of advanced data compression techniques. Traditional techniques often struggle to maintain a balance between compression efficiency and data integrity, particularly when dealing with diverse and large datasets. To address this issue, this paper presents an adaptive data compression algorithm based on discrete wavelet transform (DWT) and Horse Herd Optimization (HHO). The proposed technique significantly improves storage by dynamically optimizing key performance metrics, such as signal-to-noise ratio (SNR), mean square error (MSE), and compression ratio (CR). By employing a robust optimization algorithm, it effectively addresses the computational challenges of processing large-scale and real-time data. This adaptability ensures the algorithm is well-suited for dynamic smart grid environments, providing a scalable and reliable solution to modern data management demands. This technique uses HHO to find the optimal thresholding for smart grid data compression. In general, DWT-based data compression is carried out using a universal threshold for ignoring particular wavelet coefficients. But, the performance of data compression varies for different threshold values. Hence, selecting an optimal threshold is a challenging task for data compression. Therefore, to solve this issue, an effective optimization algorithm is needed. In this work, a Multi-Objective Horse Herd Optimization (MO-HHO) algorithm is proposed to find the optimal threshold. The suggested MO-HHO algorithm accurately determines the global optimum threshold. Hence, it maintains a good compromise between SNR, MSE and CR. The effectiveness of the proposed algorithm is examined using three different data sets. Various datasets from the IEEE Power quality wave data, Household Electric Power Consumption data and Real-time dataset from an experimental set-up were used to test the proposed algorithm. The outcome demonstrates that the suggested MO-HHO algorithm performs better than the other conventional methods.

The trajectory data of vessel AIS (automatic identification system) has important theoretical and application value for information supporting decisions. However, large sizes lead to difficulties in storing, querying, and processing. To solve the problems of high compression ratio and longtime consumption of the existing online trajectory compression algorithm, an SPM (scan-pick-move) trajectory data compression algorithm added sliding window is proposed. In order to better compress vessel trajectory data regarding compression efficiency, the sliding window is added to the classical SPM algorithm. In order to reduce trajectory data storage space, the maximum offset distance reference trajectory point is used as the criterion of whether the current trajectory point can be compressed. In this paper, the multi-dimensional space-time characteristics of trajectory data, such as distance error, compression ratio and compression time, are selected to evaluate the trajectory compression method from three levels: geometric characteristics, motion characteristics and compression efficiency. Compared with the existing SPM trajectory data compression algorithm, parallel experiments are conducted based on AIS data gathered over the duration of a month in the Japan Osaka Bay. The SPM trajectory compression algorithm added sliding window can significantly reduce the compression time and outperforms other existing trajectory compression algorithms in term of average compression error at high compression strengths. Also, the proposed method has high compression efficiency in the range of commonly used compression thresholds.

<p>The projection into 2030 of the climate data volume increase brings an important challenge to the climate science community. This is particularly true for the CMIP7 that is projected to need about an Exabyte of storage capacity. Error-bounded lossy compression is explored as a potential solution to the above problem by different climate research teams. Several lossy compression schemes have been proposed leveraging different forms of decorrelation (transforms, prediction, HoSVD, DNN), quantization (linear, non-linear, vector), and encoding (dictionary-based, variable length, etc.) algorithms. Our experience with different applications shows that the compression methods often need to be customized and optimized to fit the specificities of the datasets to compress and the user requirements on the compression quality, ratio, and throughput. However, none of the existing lossy compression software for scientific data has been designed to be customizable. To address this issue, we developed SZ3, an innovative customizable, modular compression framework. SZ3 is a full C++ refactoring of SZ2 enabling the specialization, addition, or removal of each stage of the lossy compression pipeline to fit the specific characteristics of the datasets to compress and the use-case requirements. This extreme flexibility allows adapting SZ3 to many different use-cases, from ultra-high compression for visualization to ultra-high-speed compression between the CPU (or GPU) and the memory. Thanks to its unique set of features: customization, high compression ratio, high compression throughput, and excellent accuracy preservation, SZ3 won a 2021 R&D100 award. In this presentation, we present SZ3 and a new data prediction-based decorrelation method that significantly improves the compression ratios for climate datasets over the state-of-the-art lossy compressors, while preserving the same data accuracy. Experiments based on CESM datasets show that SZ3 can lead to up to 300% higher compression ratios than SZ2 with the same compression error bound and similar compression throughput.</p>

One of the goals of telemedicine is to enable remote visualization and browsing of medical volumes. There is a need to employ scalable compression schemes and efficient client-server models to obtain interactivity and an enhanced viewing experience. First, we present a scheme that uses JPEG2000 and JPIP (JPEG2000 Interactive Protocol) to transmit data in a multi-resolution and progressive fashion. The server exploits the spatial locality offered by the wavelet transform and packet indexing information to transmit, in so far as possible, compressed volume data relevant to the clients query. Once the client identifies its volume of interest (VOI), the volume is refined progressively within the VOI from an initial lossy to a final lossless representation. Contextual background information can also be made available having quality fading away from the VOI. Second, we present a prioritization that enables the client to progressively visualize scene content from a compressed file. In our specific example, the client is able to make requests to progressively receive data corresponding to any tissue type. The server is now capable of reordering the same compressed data file on the fly to serve data packets prioritized as per the client's request. Lastly, we describe the effect of compression parameters on compression ratio, decoding times and interactivity. We also present suggestions for optimizing JPEG2000 for remote volume visualization and volume browsing applications. The resulting system is ideally suited for client-server applications with the server maintaining the compressed volume data, to be browsed by a client with a low bandwidth constraint.

In this article we investigate the trade-off between the compression ratio and distortion of a recently published compression technique specifically devised for multichannel electroencephalograph (EEG) signals. In our previous paper, we proved that, when singular value decomposition (SVD) is already performed for denoising or removing unwanted artifacts, it is possible to exploit the same SVD for compression purpose by achieving a compression ratio in the order of 10 and a percentage root mean square distortion in the order of 0.01 %. In this article, we successfully demonstrate how, with a negligible increase in the computational cost of the algorithm, it is possible to further improve the compression ratio by about 10 % by maintaining the same distortion level or, alternatively, to improve the compression ratio by about 50 % by still maintaining the distortion level below the 0.1 %.

Text compression is a necessity for efficient data storage and transmission. Especially in the digital era, volumes of digital text have increased incredibly. Traditional text compression methods, including Huffman coding and Lempel-Ziv-Welch, have certain limitations regarding their adaptability and efficiency in dealing with such complexity and diversity of data. In this paper, we propose a hybrid method that combines Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) to optimize the compression of text using the broad exploration capabilities of GA and fast convergence properties of PSO. The experimental results reflect that the proposed hybrid approach of GA-PSO yields much better performance in compression ratio than the standalone methods by reducing the size to about 65% while retaining integrity in the original content. The proposed method is also highly adaptable to various text forms and outperformed other state-of-the-art methods such as the Grey Wolf Optimizer, the Whale Optimization Algorithm, and the African Vulture Optimization Algorithm. These results support that the hybrid method GA-PSO seems promising for modern text compression.

Voltage source inverter (VSI) had been used in dc traction power substation to deliver the trains braking energy back to the utility grid. To mitigate low order harmonics components, pulse-width-modulation (PWM) technique is commonly used in VSI controlled. As a result, the ac voltage and current waveforms may contain high frequency ripples. This paper proposes to mitigate the high frequency harmonics using LCL-filter with series <em>R</em>-damper. This filter offers good attenuation on harmonics with smaller size compare to other passive filter topologies. Theoretical analysis and simulation verification are conducted in designing the proposed filter. System level simulation had also been carried out. Sinusoidal grid current and voltage waveforms are recorded (THD<2%). This paper also includes damping losses analysis in conjunction with resonance peak suppression in designing the <em>R</em>-damper.

Impact of Developer Choices on Energy Consumption of Software on Servers

Embedded systems are constrained by the available memory. Code compression techniques address this issue by reducing the code size of application programs. Dictionary-based code compression techniques are popular because they offer both good compression ratio and fast decompression scheme. Recently proposed techniques by J. Prakash et al. (2003) improve standard dictionary-based compression by considering mismatches. This paper makes two important contributions: i) it provides a cost-benefit analysis framework for improving the compression ratio by creating more matching patterns, and ii) it develops an efficient code compression technique using bitmasks to improve the compression ratio without introducing any decompression penalty. To demonstrate the usefulness of our approach we have used applications from various domains and compiled for a wide variety of architectures. Our approach outperforms the existing dictionary-based techniques by an average of 15%, giving a compression ratio of 55% - 65%

The features of mass spatial data and the limit of Internet bandwidth are key issues which restrict the WebGIS application performance. How to implement efficient vector data compression is important during the stage of data transmission. To get high compression ratio, we propose one multilevel vector data compression method which combine lossy compression and lossless compression technology. According to the resolution of the map, we ignore some vector coordinates based on visualization. Moreover, we apply coding technique to geographic coordinates to achieve higher compression ratio. After illustrating the design and implementation of this method, we conduct experiments to compare the performance of traditional compression technique and the proposed method. The result shows the significant compression ratio (about 90%) which enhance the data transmission efficiency to a large extent.

Recent mixture of experts (MoE)-based transformer models have achieved remarkable performance across various applications. However, as model size increases, the associated memory demand escalates dramatically, limiting their deployment in resource-constrained edge environments. To address this challenge, this study investigates the trade-off between performance and computational overhead under extreme compression conditions by applying ternary quantization and additional compression schemes to MoE models. Experimental results demonstrate that Huffman coding achieves a higher compression ratio but suffers from substantial decoding latency, while dictionary coding provides a lower compression ratio with significantly faster decoding. These findings highlight that efficient deployment of MoE models requires an integrated compression framework that jointly optimizes compression ratio and overhead. Furthermore, based on these observations, potential research directions are suggested, including hybrid coding formats and look-up table-based dedicated hardware accelerators for efficient MoE model compression.

Summary form only given. Research on text compression mainly concerns documentation applications; it has seldomly considered other applications. Significant efforts have previously been made to increase both the data capacity and the information density of bar code symbologies. The results of these efforts created the formats of 2D bar codes. We take PDF417 (Pavlidis et al. 1992) developed by Symbol Technologies as a example. PDF417 is the most popular of the 2D bar code symbologies. However the storage capacity in PDF417 has limited its wider application. Here, we propose a text compression technique with the back searching algorithm and new storage protocols. Studies on how a word-based multiple-dictionary text compression technique can be used to increase the storage capacity in a 2D bar code are described. In order to speed up the search of the text, a hashing function is also described. For application in data base retrieval the proposed technique is particularly useful. For data stored in 2D bar codes which are in the form of limited forms such as part numbers, location, name and reference, the compression ratio can be as high as 2 because the hit ratio can be 100%. For the decoder design, the complexity need not be complex as the decoder just requires to know the 'light' and 'dark'. To let the dictionaries become more 'intelligent', a sub-dictionary is proposed which allows the encoded text to be more independent.