PDF-Based Data Stream Compression for Interconnect Traffic Reduction
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Solution Overview
Problem
As data transmission rates increase over high-speed communication networks, efficiently transmitting data over system interconnects becomes challenging due to the growing volume of data, necessitating effective compression and decompression mechanisms.
Innovation Solution
The implementation of a data compression accelerator (DCA) that calculates a probability distribution function (PDF) for incoming data, matches it to predefined templates, and uses an entropic encoder to compress the data, along with an identifier for decompression at the receiving end, facilitating efficient data transmission over both wired and wireless connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data transmission rates are increased over high-speed communication networks, then data delivery speed is improved, but data traffic volume increases causing transmission inefficiency
Solution Approach 1:
The patent extracts redundant information from data streams by computing probability distribution functions and identifying patterns. The DCA removes unnecessary data elements while preserving essential information, effectively separating useful data from redundant traffic to reduce overall volume while maintaining transmission speed.
Solution Approach 2:
The patent transforms data representation by changing parameters from raw data format to compressed format based on probability distribution functions. By modifying how data is encoded (using PDF-based entropy coding), the system reduces traffic volume while maintaining the ability to reconstruct original data at the receiver.
2Productivity
If data compression is applied to reduce traffic volume, then transmission efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent segments the compression process into distinct functional modules: PDF computation unit, pattern matching unit, and entropy coding unit. This segmentation allows each component to perform a specific function, reducing overall system complexity while maintaining compression effectiveness. The receiver side is similarly segmented for decompression operations.
Solution Approach 2:
The patent performs preliminary computation of probability distribution functions and pattern matching before actual data compression. By pre-computing these parameters and storing them in lookup tables, the system reduces real-time computational complexity during data transmission while maintaining high compression ratios.
3Reliability
If lossless compression is used to ensure data accuracy, then data integrity is improved, but compression ratio decreases compared to lossy compression
Solution Approach 1:
The patent implements feedback mechanisms where the transmitter and receiver continuously exchange information about the PDF parameters and encoding schemes used. This feedback allows the receiver to accurately reconstruct the original data without loss, ensuring data integrity while achieving better compression ratios through adaptive encoding based on observed data patterns.
Data Source
AI summary
Methods and apparatus for compressing data streams. In an embodiment, a method includes calculating a probability distribution function (PDF) for scaler data, matching the PDF to PDF templates to determine a closest matching PDF template, and selecting an encoder corresponding to the closest matching PDF template wherein a corresponding encoder identifier is determined. The method also includes encoding the scaler data with the encoder to generate an encoded stream, and transmitting the encoded stream and the encoder identifier.