Template-Matched Data Stream Compression for Interconnect Bandwidth
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Solution Overview
Problem
As data rates increase and data volume grows, existing systems face challenges in efficiently transmitting data over system interconnects, both within integrated circuits and across high-speed communication networks like LTE and 5G, leading to difficulties in accurately delivering and deciphering data streams.
Innovation Solution
A data compression accelerator (DCA) is implemented to compress data using probability distribution functions (PDFs) and entropic encoders, which calculates a PDF for incoming data, matches it to predefined templates, selects an appropriate encoder, and transmits the encoded stream along with an identifier for efficient decompression at the receiving end, enabling lossless data compression and efficient transmission over system interconnects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data compression is implemented to reduce traffic over system interconnects, then data transmission efficiency is improved, but device complexity increases due to the need for PDF computation circuits, matching circuits, and encoding circuits
Solution Approach 1:
The data compression apparatus is divided into distinct functional modules: a probability distribution function computation circuit that calculates PDFs for incoming data, a matching circuit that compares the calculated PDF against stored templates, and an encoding circuit that applies the selected encoder. This segmentation allows each module to perform a specific task efficiently, resolving the contradiction by organizing complexity into manageable, specialized components rather than a monolithic system.
Solution Approach 2:
Probability distribution function templates are pre-computed and stored in memory before actual data compression operations. When data arrives, the system only needs to match the calculated PDF against these pre-prepared templates rather than computing encoders from scratch. This preliminary action significantly reduces real-time processing complexity while maintaining high compression efficiency.
2Reliability
If lossless data compression is used to accurately deliver data streams, then data accuracy is improved, but processing time increases due to the computational requirements of PDF calculation and matching
Solution Approach 1:
The system pre-computes and stores multiple probability distribution function templates corresponding to different data patterns in memory. During data transmission, the apparatus only needs to calculate the PDF of incoming data and match it against these pre-existing templates, rather than performing full encoder computations. This preliminary preparation dramatically reduces processing time while maintaining lossless compression accuracy.
Solution Approach 2:
The system changes the parameter representation by working with probability distribution functions as intermediate representations rather than directly computing encoder parameters. This parameter transformation allows for more efficient matching and selection, reducing the computational time required while preserving the accuracy needed for lossless compression.
3Adaptability or versatility
If multiple encoder templates are stored in memory to handle different data patterns, then adaptability is improved, but memory requirements increase
Solution Approach 1:
The system stores multiple probability distribution function templates in memory, each optimized for specific data patterns or characteristics. Rather than using a single generic encoder, the apparatus selects the template that best matches the local characteristics of the incoming data. This local specialization allows high adaptability to different data types while keeping each individual template compact, balancing memory usage with versatility.
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.