Memory Coding Banks via Partial Data Segmentation
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Solution Overview
Problem
Current memory systems require significant storage capacity and incur additional costs due to the need for generating and storing codes for data redundancy, which also leads to reduced performance during operation.
Innovation Solution
The method involves generating and storing coding banks by summing data values from multiple data banks and storing these codes in a secondary memory, allowing for efficient data reconstruction and reduced storage requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If codes are generated for an entirety of data stored in memory, then data redundancy and reliability are improved, but storage capacity requirements and cost increase
Solution Approach 1:
The invention divides the data storage into multiple data banks (first through eighth data banks) and generates codes selectively for specific combinations of these banks rather than for the entire dataset. This segmentation allows the system to maintain data redundancy for critical data combinations while reducing overall storage capacity requirements.
Solution Approach 2:
The patent implements partial coding by generating codes only for specific data bank combinations (e.g., first code from first and second data banks, second code from third and fourth data banks) rather than coding all data. This partial action approach maintains sufficient reliability for data recovery while significantly reducing the quantity of stored codes compared to full-data coding.
2Reliability
If codes are generated for an entirety of data stored in memory, then data redundancy is improved, but performance during operation deteriorates
Solution Approach 1:
By segmenting the coding operation into discrete code generation for specific data bank pairs, the system enables parallel processing of code generation and data access operations. This segmentation reduces operational overhead and improves performance compared to generating codes for the entire dataset.
Solution Approach 2:
The partial coding approach generates only the necessary codes for specific data combinations, reducing the computational burden and time required for code generation operations. This partial action maintains data redundancy reliability while improving overall system performance during data operations.
3Reliability
If sizeable storage capacity is allocated for codes, then data redundancy is improved, but cost increases
Solution Approach 1:
The segmentation of data into multiple banks with selective code generation reduces the total storage capacity needed for codes, directly lowering manufacturing costs while maintaining data redundancy through strategic code placement in the memory architecture.
Solution Approach 2:
By implementing partial coding that generates codes only for specific data bank combinations rather than all data, the system reduces the storage capacity allocated to codes, thereby reducing manufacturing cost while preserving sufficient data redundancy for reliable operation.
Data Source
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AI summary
An intelligent code apparatus, method, and computer program are provided for use with memory. In operation, a subset of data stored in a first memory is identified. Such subset of the data stored in the first memory is processed, to generate a code. The code is then stored in a second memory, for use in reconstructing at least a portion of the data.