Eigenvector Cluster Compression for High-Speed Data Streams
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Solution Overview
Problem
As data transmission rates increase over high-speed communication networks, existing systems face challenges in efficiently transmitting and deciphering data streams, particularly over system interconnects within integrated circuits or between them, leading to inefficiencies in data transfer.
Innovation Solution
A data compression accelerator (DCA) is introduced that computes probability distribution functions (PDFs) for incoming data streams, matches them to predefined templates, and uses entropic encoders to compress the data, along with an identifier for decompression, enabling efficient transmission and decompression of scalar, vector, and matrix data over system interconnects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data transmission rate is increased over high-speed communication networks, then information delivery capability is improved, but data compression and deciphering complexity increases
Solution Approach 1:
The patent segments the data stream processing into distinct functional blocks: PDF computation circuit, matching circuit, selection circuit, and encoder. Each block handles a specific aspect of the compression process, allowing parallel processing and reducing overall system complexity while maintaining high transmission rates
Solution Approach 2:
The system performs preliminary computation of probability distribution functions and matching against predefined PDFs before actual data compression. This pre-processing enables the selection of optimal encoding parameters in advance, simplifying the real-time compression operation and enabling faster transmission
2Quantity of substance
If data volume over system interconnects is increased, then data transmission capacity is improved, but traffic efficiency deteriorates
Solution Approach 1:
The patent dynamically changes encoding parameters based on the computed PDF of the input data stream. By adapting the probability distribution model to match the actual data characteristics, the system achieves optimal compression ratios for different data types and patterns, improving traffic efficiency while handling increased data volumes
Solution Approach 2:
The compression system is designed to handle multiple data types and transmission scenarios using a unified approach. The PDF computation and matching framework can process various data distributions, making the system universally applicable across different interconnect standards and data formats, thereby improving overall traffic efficiency
3Reliability
If lossless data compression is implemented, then data accuracy is improved, but compression processing time increases
Solution Approach 1:
The system computes PDFs using a finite window of recent data samples rather than the entire data stream. This partial action approach provides sufficiently accurate probability estimates for effective compression while significantly reducing computation time compared to analyzing complete data histories, thus balancing accuracy and processing speed
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.