Cross-Boundary Data Compression Using Parallel Sub-Chunk Matching
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Solution Overview
Problem
Existing data compression techniques face a tradeoff between compression efficiency and the size of search engines, leading to increased area, power consumption, and latency, and struggle to effectively remove redundancy across the boundary of compression search engines.
Innovation Solution
The method involves splitting data frames into sub-chunks, comparing them to identify and remove duplicates, generating matching sub-chunk information for reconstruction, grouping remaining sub-chunks into units, removing repeated patterns within these units, and combining them with the matching information to generate a compressed data frame, using smaller search engines to improve throughput and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If larger search engines are used to improve compression efficiency, then better compression efficiency is achieved, but area and power consumption requirements increase
Solution Approach 1:
The patent divides the data frame into multiple sub-chunks and processes them through multiple smaller search engines in parallel. This segmentation allows the system to achieve compression efficiency comparable to a single large search engine while using smaller, more area-efficient individual search engines. The data frame is split into sub-chunks that are independently processed, and the results are combined to achieve overall compression.
2Measurement precision
If larger search engines are used to improve compression efficiency, then better compression efficiency is achieved, but power consumption increases
Solution Approach 1:
The patent segments the compression task across multiple smaller search engines that operate in parallel. This approach reduces the power consumption of individual search engines while maintaining overall compression efficiency through parallel processing. The total power consumption is distributed across multiple lower-power units rather than concentrated in a single high-power unit.
3Measurement precision
If larger search engines are used to improve compression efficiency, then better compression efficiency is achieved, but latency increases
Solution Approach 1:
The patent divides the data frame into sub-chunks that can be processed simultaneously by multiple search engines. This parallel processing significantly reduces the overall latency compared to a single large search engine processing the entire data frame sequentially. The segmentation enables time-efficient parallel execution while maintaining compression quality.
4Area of stationary object
If smaller search engines are used to reduce area and power, then area and power consumption are reduced, but compression efficiency deteriorates
Solution Approach 1:
The patent combines the results from multiple smaller search engines to achieve compression efficiency equivalent to or better than a single large search engine. By merging the compression outputs and coordinating the processing across multiple engines, the system recovers the compression efficiency that would otherwise be lost by using smaller individual search engines.
Solution Approach 2:
The patent segments the data frame into sub-chunks that are processed by smaller search engines in parallel. This segmentation allows smaller search engines to achieve effective compression by working on smaller, more manageable data portions simultaneously, thereby recovering compression efficiency despite using smaller individual engines.
5Measurement precision
If redundancy removal is performed across search engine boundaries, then compression efficiency is improved, but device complexity increases
Solution Approach 1:
The patent segments the data frame into sub-chunks that are processed by multiple search engines with defined boundaries. The system manages complexity by organizing the parallel processing of segmented data, allowing redundancy removal across boundaries while maintaining a structured approach that prevents unmanageable system complexity.
Data Source
AI summary
Data compression techniques are provided that remove redundancy across the boundary of compression search engines. An illustrative method comprises splitting the data frame into a plurality of sub-chunks; comparing at least two of the plurality of sub-chunks to one another to remove at least one sub-chunk from the plurality of sub-chunks that substantially matches at least one other sub-chunk to generate a remaining plurality of sub-chunks; generating matching sub-chunk information for data reconstruction identifying the at least one removed sub-chunk and the corresponding substantially matched at least one other sub-chunk; grouping the remaining plurality of sub-chunks into sub-units; removing substantially repeated patterns within the sub-units to generate corresponding compressed sub-units; and combining the compressed sub-units with the matching sub-chunk information to generate a compressed data frame. The data frame optionally comprises one or more host pages compressed substantially simultaneously, and the compressed data frame for a plurality of host pages compressed substantially simultaneously comprises a host page address for each host page.