Half Product Code Error Correction With Soft-Hard Decoding Split
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Solution Overview
Problem
Data storage devices face challenges in achieving accurate data reconstruction with reduced processing logic overhead, as soft decision decoding requires more processing resources compared to hard decision decoding, especially for larger data blocks.
Innovation Solution
Implementing a data storage device that uses a combination of soft decision and hard decision encoding and decoding algorithms within a half product code framework, where convolutional encoding is used for soft decision encoding and BCH encoding for hard decision encoding, allowing for precise data reconstruction without the additional logic overhead of traditional soft decision technologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If soft decision decoding is used to achieve accurate data reconstruction, then data reliability is improved, but processing logic complexity increases
Solution Approach 1:
The patent divides the error correction code into two distinct segments: a first error correction code generated using hard decision decoding (simpler logic) and a second error correction code generated using soft decision decoding (more accurate but complex logic). This segmentation allows the system to benefit from soft decision accuracy for critical data while using hard decision for less critical portions, thereby reducing overall processing logic complexity while maintaining data reliability.
Solution Approach 2:
The patent applies different quality levels of error correction to different portions of the data. Critical data portions receive soft decision decoding with higher correction capability, while non-critical portions use hard decision decoding. This local differentiation optimizes the balance between reliability and processing complexity by concentrating computational resources where they are most needed.
2Device complexity
If hard decision decoding is used to reduce processing logic overhead, then device complexity is reduced, but data reconstruction accuracy deteriorates
Solution Approach 1:
The patent segments the data into multiple portions and applies different decoding strategies to each segment. By dividing the error correction code into first and second codes with different decoding methods, the system achieves accurate reconstruction of critical data without requiring all data to undergo complex soft decision decoding, thus reducing overall device complexity while maintaining necessary accuracy.
Solution Approach 2:
The patent implements local quality enhancement by applying soft decision decoding specifically to portions of data where high accuracy is critical, while using hard decision decoding for other portions. This selective approach ensures data reconstruction accuracy is maintained where needed without incurring the full processing logic overhead of universal soft decision decoding.
Data Source
AI summary
Technologies for providing efficient error correction with half product codes include an apparatus having a memory to store data and a controller to manage read and write operations of the memory. The controller is to obtain, in response to a write request, data to write to the memory. The controller is further to encode the data with a half product code to define a matrix that includes at least one matrix element based on a soft decision error correction encoder algorithm and at least one other matrix element based on a hard decision error correction encoder algorithm. Additionally, the controller is to write the half product code to the memory.