SSD Controller Higher-Level Redundancy Computation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current flash memory technologies face challenges in maintaining reliable operation and data integrity due to uncertainties in sensing and changes in electron storage over time, leading to increased probabilities of data corruption, especially as storage capacity and density increase, and existing error correction techniques may not effectively handle failures in NAND flash memory elements.
Innovation Solution
The implementation of dynamic higher-level redundancy mode management with independent silicon elements in SSD controllers, which computes and stores higher-level redundancy information using parity coding and weighted-sum techniques to ensure reliable operation even in the presence of failures, by transitioning between different redundancy modes to maintain data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If higher-level redundancy information is computed using parity coding and weighted-sum techniques, then data integrity and reliability are improved, but computational complexity and processing time increase
Solution Approach 1:
The redundancy information is divided into multiple units, each protecting a specific portion of data. The system computes and stores separate redundancy units corresponding to different data segments, allowing targeted error correction without processing the entire dataset, thus reducing computational complexity while maintaining reliability.
Solution Approach 2:
The system pre-computes and stores higher-level redundancy information during normal operations, so that when errors occur, the correction process can immediately utilize pre-generated redundancy data without performing complex computations in real-time, thereby reducing processing time and computational burden during error correction.
2Adaptability or versatility
If dynamic redundancy mode management is implemented, then adaptability to failure conditions is improved, but system complexity and control overhead increase
Solution Approach 1:
The system dynamically transitions between different redundancy modes based on detected failure conditions. When failures are detected, the system adapts by switching to alternative redundancy mechanisms or adjusting redundancy allocation, providing flexibility and adaptability to varying failure scenarios while managing system complexity through automated mode switching.
Solution Approach 2:
The system continuously monitors storage device health and uses feedback from failure detection to automatically adjust redundancy management strategies. This closed-loop approach allows the system to adapt to changing conditions without requiring complex manual intervention, reducing control overhead while maintaining high adaptability to failure conditions.
3Duration of action of stationary object
If redundancy information is stored in non-volatile memory, then data persistence is improved, but storage capacity requirements increase
Solution Approach 1:
The system stores redundancy information locally within the same non-volatile memory device rather than requiring separate storage resources. By utilizing unused capacity within existing memory blocks and implementing localized redundancy storage, the system maintains data persistence without proportionally increasing overall storage capacity requirements.
Solution Approach 2:
The redundancy information is nested within the data structure itself, with redundancy units integrated into the same storage blocks as the protected data. This nested approach allows the system to maintain persistence through the same storage medium without requiring additional separate storage capacity, effectively hiding the redundancy storage within the existing storage infrastructure.
4Reliability
If error correction operations are performed, then data reliability is improved, but processing time and latency increase
Solution Approach 1:
The system pre-computes and stores redundancy information during normal read operations, so that when errors are detected, the correction process can immediately utilize the pre-generated redundancy data without performing time-consuming computations, thereby reducing error correction latency while maintaining high data reliability.
Solution Approach 2:
When errors are detected, the system rapidly retrieves and applies pre-computed redundancy information to correct the data, skipping the time-consuming process of real-time redundancy computation. This approach rushes through the error correction process by utilizing预先 prepared redundancy data, significantly reducing processing time while maintaining correction accuracy.
Data Source
AI summary
Higher-level redundancy information computation enables a Solid-State Disk (SSD) controller to provide higher-level redundancy capabilities to maintain reliable operation in a context of failures of non-volatile (e.g. flash) memory elements during operation of an SSD. A first portion of higher-level redundancy information is computed using parity coding via an XOR of all pages in a portion of data to be protected by the higher-level redundancy information. A second portion of the higher-level redundancy information is computed using a weighted-sum technique, each page in the portion being assigned a unique non-zero “index” as a weight when computing the weighted-sum. Arithmetic is performed over a finite field (such as a Galois Field). The portions of the higher-level redundancy information are computable in any order, such as an order based on order of read operation completion of non-volatile memory elements.