Semantic Field Compression for Low-Latency Data Decompression
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Solution Overview
Problem
Existing data compression methods are inefficient when dealing with semantically meaningful data fields, as they typically compress data values as a whole, failing to exploit value locality effectively, which leads to increased compression and decompression latency in computer systems and communication networks.
Innovation Solution
The approach involves dividing data values into semantically meaningful fields, compressing each field independently, and grouping fields with shared semantic meaning for parallel compression and decompression, using techniques like Huffman encoding to enhance compressibility and reduce latency without adding metadata overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data values are compressed as a whole using existing compression methods, then the compression process is simple to implement, but the compression efficiency is poor and value locality cannot be exploited effectively
Solution Approach 1:
The patent divides each data value into multiple semantically meaningful fields (e.g., integer fields, floating-point fields, vector fields, matrix fields) and compresses each field independently using appropriate compression schemes. This segmentation allows the system to exploit value locality within each field type while maintaining overall compression efficiency without requiring complex whole-value compression algorithms.
2Loss of time
If compression is applied to entire data values, then the compression algorithm is easy to implement, but the decompression latency increases due to inability to process fields in parallel
Solution Approach 1:
By segmenting data into independent semantic fields, the patent enables parallel decompression of different field types simultaneously. Each field can be decompressed independently using optimized algorithms suited to its characteristics, significantly reducing overall decompression latency compared to sequential processing of entire data values.
Solution Approach 2:
The patent applies different compression parameters and algorithms tailored to each semantic field type (e.g., different schemes for integer vs. floating-point fields). This parameter customization optimizes both compression ratio and decompression speed for each field type while keeping the overall system implementation manageable through standardized field processing routines.
3Productivity
If data fields are compressed independently with semantic grouping, then parallel processing can be achieved, but the device complexity increases due to multiple compression units
Solution Approach 1:
The patent implements separate compression units for different semantic field types (integer compression unit, floating-point compression unit, etc.), allowing parallel processing of different field types simultaneously. This segmentation into specialized units achieves parallelism while keeping each unit's complexity manageable and focused on specific field characteristics.
Solution Approach 2:
Each compression unit is designed to handle multiple related field types with similar characteristics (e.g., the integer compression unit handles various integer formats). This multi-functionality reduces the total number of specialized units needed while still enabling parallel processing across different semantic categories.
4Loss of substance
If existing compression methods are used on complete data values, then no additional metadata overhead is required, but the compression ratio is suboptimal due to ignoring value locality
Solution Approach 1:
The patent segments data into semantic fields and groups fields with similar characteristics together. This segmentation exploits value locality within each group, achieving significantly better compression ratios compared to treating entire data values as single units, while the grouping mechanism remains relatively simple and systematic.
Data Source
AI summary
Methods, devices and systems enhance compression and decompression of data values when they comprise a plurality of semantically meaningful data fields. Compression is sometimes not applied to each data value as a whole, but instead to at least one of the semantically meaningful data fields of each data value, and in isolation from the other ones. Data fields can be organized that share the same semantic meaning together to accelerate compression and decompression as multiple compressors and decompressors can be used in parallel. A system can be used where methods and devices are tailored to perform compression and decompression of the semantically meaningful data fields of floating-point numbers after first partitioning further at least one of said data fields into two or a plurality of sub-fields to increase the degree of value locality and improve compressibility of floating-point values.