Eigenvector Tag Compression with PDF-Matched Entropy Encoding
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Solution Overview
Problem
The challenge in data transmission over high-speed communication networks and system interconnects is to efficiently manage the increasing data rates and volumes, requiring effective compression and decompression methods to reduce traffic and ensure accurate data delivery.
Innovation Solution
The implementation of a data compression accelerator (DCA) that uses probability distribution functions (PDFs) to compress data streams by matching calculated PDFs to predefined templates, selecting the closest match, and employing entropic encoders for lossless compression, along with a corresponding decompression process at the receiver.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data compression is applied to reduce traffic over system interconnects, then data transmission efficiency is improved, but device complexity increases due to the need for PDF computation, matching, and encoder selection circuits
Solution Approach 1:
The compression apparatus is divided into distinct functional modules: a PDF computation module that calculates probability distributions, a matching module that compares PDFs to templates, and an encoder selection module that chooses appropriate encoders. This segmentation allows each module to perform its specific function efficiently while maintaining overall system manageability despite the increased complexity.
2Adaptability or versatility
If multiple encoder types are supported for different data patterns, then compression adaptability is improved, but loss of information risk increases due to potential mismatches between encoder and data characteristics
Solution Approach 1:
The system computes the actual probability distribution function of the input data and compares it against stored PDF templates to determine the best matching encoder type. This feedback mechanism ensures that the selected encoder is optimally suited to the data characteristics, minimizing information loss during compression and decompression operations.
Solution Approach 2:
The system changes the parameter used for encoder selection from fixed or heuristic-based choices to dynamic PDF-based characterization. By computing and matching PDFs, the system adapts its compression approach based on the actual statistical properties of the data, improving both adaptability and information preservation.
3Measurement precision
If PDF computation and matching is performed for every data stream, then compression precision is improved, but processing time increases due to the computational overhead of PDF calculation and template matching
Solution Approach 1:
PDF templates are pre-computed and stored in the system before actual compression operations begin. This preliminary action allows the matching phase to quickly compare against ready-made templates rather than computing PDFs from scratch for every data stream, significantly reducing processing time while maintaining compression precision.
Data Source
AI summary
Methods and apparatus for compressing data streams. In an embodiment, a method includes calculating a decomposition of matrix data to generate eigenvectors and associated eigenvalues, determining clusters of the eigenvectors based on weighting the eigenvalues, calculating an eigenvector centroid for each cluster so that a dictionary of centroids is generated, and tagging the eigenvectors with tags, respectively, that identify an associated eigenvector centroid for each eigenvector. The method also includes counting a number of eigenvectors associated with each eigenvector centroid to construct a probability distribution function (PDF) of centroids, matching the PDF of centroids to PDF templates to determine a closest matching PDF template, determining an encoder corresponding to the closest matching PDF template wherein a corresponding encoder identifier is identified, encoding the tags with the encoder to generate an encoded data stream, and transmitting the encoded data stream, the encoder identifier, the dictionary of centroids, and the eigenvalues.