Memory Controller Wear-Leveling via Dynamic L2P Table Adjustment
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Solution Overview
Problem
Existing memory systems suffer from uneven aging due to uneven access patterns, leading to reduced lifespan and inefficient memory operations, as some physical addresses are accessed more frequently than others, causing premature wear in certain memory portions.
Innovation Solution
A memory controller adjusts the logical-to-physical (L2P) translation table based on access patterns to distribute access more evenly across memory banks, using deep learning algorithms to optimize address assignments and promote wear leveling, thereby improving memory operation speeds and extending device lifespan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If data is stored in the same memory bank, then memory structure is simple, but memory operation time increases due to sequential access constraints
Solution Approach 1:
The patent segments data storage across multiple memory banks instead of concentrating all data in a single bank. The logical-to-physical address translation table maps logical addresses to physical addresses distributed across different memory banks, enabling parallel operations and reducing sequential access constraints.
2Speed
If frequent access patterns are maintained, then data retrieval speed is fast, but memory wear becomes uneven and device lifespan decreases
Solution Approach 1:
The patent dynamically updates the logical-to-physical address translation table based on observed access patterns. The system monitors which physical addresses are accessed most frequently and adjusts mappings to distribute access more evenly across memory banks, preventing localized wear while maintaining fast retrieval speeds through intelligent address translation.
3Productivity
If access patterns are optimized for speed, then memory operation efficiency improves, but wear leveling deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors memory access patterns and uses this information to adjust the logical-to-physical address translation table. This feedback loop enables the system to optimize both speed and wear leveling by learning from actual access behavior and adapting address mappings accordingly.
Data Source
AI summary
As described herein, an apparatus may include a memory that includes a first portion, a second portion, and a third portion. The apparatus may also include a memory controller that includes a first logical-to-physical table stored in a buffer memory. The memory controller may determine that the first portion is accessed sequential to the second portion and may adjust the first logical-to-physical table to cause a memory transaction performed by the memory controller to access the third portion as opposed to the first portion.