NAND Decoder Using Historical Soft Information for Faster ECC
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current error-correcting codes, such as ECC and FEC, are computationally complex and require improvements in decoding efficiency, especially in non-volatile storage systems where data corruption is common.
Innovation Solution
A method that utilizes soft information generated from previous decoding processes to improve decoding efficiency by retrieving and updating reliability information for data chunks, using a unique identifier associated with physical locations in a storage system, and incorporating this information into the decoding process to enhance bit reliability assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional error-correcting codes (ECC, FEC) are used to correct data corruption, then data reliability is improved, but computational complexity increases
Solution Approach 1:
The system performs preliminary decoding attempts using simplified decoding algorithms before applying full complex decoding. Historical soft information from previous decoding attempts is retrieved and used to guide subsequent decoding operations, reducing the need for computationally intensive full decoding processes while maintaining error correction capability
Solution Approach 2:
The system uses its own historical decoding information and soft data from previous operations to assist current decoding processes. By storing and reusing reliability information from prior decoding attempts, the system serves itself rather than requiring always-full computational resources, reducing overall computational complexity while maintaining reliability
2Reliability
If complex decoding algorithms are used to handle data corruption, then error correction capability is improved, but decoding time increases
Solution Approach 1:
The system retrieves pre-computed soft information and historical decoding data before performing full decoding operations. This preliminary retrieval of reliability information allows the decoder to start with informed assumptions, reducing the iterative steps needed and thereby reducing decoding time while maintaining error correction capability
Solution Approach 2:
The system implements feedback by storing soft information and decoding outcomes from previous operations, then using this feedback to guide future decoding attempts. This feedback mechanism allows the system to learn from past decoding experiences and optimize future decoding paths, reducing overall decoding time while maintaining high error correction capability
3Productivity
If soft information from previous decoding processes is retrieved and used, then decoding efficiency is improved, but memory access overhead increases
Solution Approach 1:
The system merges the soft information storage structure with the existing decoding framework by integrating soft information retrieval into the standard decoding workflow. By combining these functions into a unified process rather than separate operations, the system reduces overall overhead while maintaining improved decoding efficiency through historical information reuse
Data Source
AI summary
Systems and methods are provided for decoding data read from non-volatile storage devices. A method may comprise receiving a chunk of data read from a physical location of a non-volatile storage device and searching a memory for soft information associated with the physical location using a unique identifier associated with the physical location. The soft information may be generated from one or more previous decoding processes on previous data from the physical location. The method may further comprise retrieving the soft information identified by the unique identifier associated with the physical location from the memory, decoding the chunk of data with the soft information indicating reliability of bits in the chunk of data and updating the soft information with decoding information generated during the decoding.