Data Compression Accelerator Using PDF-Matched Entropic Encoding
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Solution Overview
Problem
Efficient data transmission over high-speed communication networks and system interconnects is challenging due to increasing data rates and volumes, particularly in wireless and integrated circuit environments.
Innovation Solution
Implementing a data compression accelerator (DCA) that calculates a probability distribution function (PDF) for incoming data, matches it with predefined templates, and uses entropic encoders to compress data, accompanied by an identifier for decompression at the receiver.
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 compression and decompression engines
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting compression parameters based on the probability distribution function of the input data. The system computes PDF from the data stream and uses it to select appropriate compression parameters, allowing the compression engine to adapt to different data characteristics and achieve optimal compression ratios without requiring a complex universal compressor for all data types.
Solution Approach 2:
The patent implements preliminary action by pre-computing and storing probability distribution function templates that represent common data patterns. When compressing data, the system compares the computed PDF against these pre-stored templates to quickly identify the best matching pattern, avoiding the need for complex real-time analysis and enabling fast compression decisions without extensive computational overhead.
2Reliability
If lossless data compression is used to ensure accurate data recovery, then data integrity is improved, but compression ratio is reduced compared to lossy compression
Solution Approach 1:
The patent uses parameter changes by adjusting the compression approach based on the computed probability distribution function. For data with high predictability (certain PDF patterns), the system applies more aggressive compression techniques that achieve better ratios while maintaining lossless properties. For less predictable data, it uses lighter compression to preserve integrity without excessive overhead, dynamically optimizing the balance between compression ratio and data integrity.
3Productivity
If probability distribution function matching is performed to select optimal encoders, then compression ratio is improved, but processing time increases
Solution Approach 1:
The patent implements preliminary action by pre-computing probability distribution function templates for common data patterns and storing them in advance. During compression, the system only needs to compare the computed PDF against these pre-prepared templates rather than performing complex real-time analysis. This preliminary preparation significantly reduces processing time while still enabling the selection of optimal encoders for achieving high compression ratios.
Solution Approach 2:
The patent applies partial action by performing PDF matching only to the extent necessary to identify the best matching template from the pre-stored set. Rather than exhaustively analyzing all possible encoder combinations or performing complete PDF optimization, the system performs sufficient matching to select the optimal encoder from the template set, achieving good compression ratios without excessive processing overhead.
Data Source
AI summary
Methods and apparatus for enhancing network data processing using compressing data streams are disclosed. The process, in one aspect, is configured to receive a data stream through a receiver via a communication network. Upon identifying compressed data and an encoder identifier carried by the data stream, a decoder database is determined in the receiver. After retrieving a decoder from the decoder database in response to the encoder identifier, the process is configured to decompress the compressed data facilitated by the decoder for generating scaler data.