Memory Controller Hot Cold Data Pool Segmentation
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Solution Overview
Problem
Current memory systems face inefficiencies in classifying data based on access frequency and managing garbage collection and wear leveling, leading to suboptimal performance, especially in distinguishing between frequently and infrequently updated data.
Innovation Solution
Implementing a memory system with a memory controller that manages hot and cold data pools, where read-only data is written to the cold data pool, excluding cold data pool blocks from garbage collection and selectively exchanging blocks between pools based on erase-write cycle counts for wear leveling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is classified into hot and cold data pools based on access frequency, then data management efficiency is improved, but device complexity increases
Solution Approach 1:
The memory device is segmented into multiple memory pools (hot data pool and cold data pool) based on data access frequency characteristics. This segmentation allows differential management strategies to be applied to different data types, improving overall efficiency while maintaining manageable complexity through clear separation of concerns.
Solution Approach 2:
The memory pool assignment is dynamic rather than static. The controller can relocate data between hot and cold pools based on changing access patterns, and memory blocks can be dynamically assigned to different pools based on wear level metrics. This dynamic approach optimizes performance while the controller manages complexity through adaptive decision-making.
2Reliability
If garbage collection is performed on all memory blocks, then data integrity is maintained, but processing time increases
Solution Approach 1:
Cold data pool blocks are extracted from the garbage collection process entirely. Since cold data is identified as infrequently accessed and stable, applying garbage collection to these blocks is unnecessary overhead. The system maintains data integrity by focusing GC only on hot data pools where writes occur frequently, thereby preserving reliability while dramatically reducing GC time.
Solution Approach 2:
Different quality of service is applied to different memory pools. Hot data pools receive full garbage collection services to maintain integrity during frequent writes, while cold data pools receive minimal or no garbage collection since their data is stable. This local differentiation optimizes the balance between reliability and time consumption.
3Reliability
If wear leveling exchanges memory blocks between pools, then memory block lifespan is extended, but operation complexity increases
Solution Approach 1:
The system performs preliminary wear leveling by proactively exchanging memory blocks between hot and cold pools based on erase-count metrics before failure occurs. By monitoring wear levels and preemptively relocating blocks, the system extends overall memory lifespan while managing complexity through structured exchange protocols between clearly defined pools.
Solution Approach 2:
Wear leveling is achieved by changing the operational parameters of memory blocks through pool relocation. Blocks with high erase counts in hot pools are moved to cold pools where they experience reduced write activity, effectively extending their usable life. This parameter change approach simplifies wear management compared to traditional block-by-block wear leveling.
Data Source
AI summary
A memory system, a memory controller and a method for operating the memory system. The memory system manages a hot data pool and a cold data pool, each of which includes at least one among a plurality of memory blocks, writes read only data to the cold data pool, and controls the hot data pool and the cold data pool in garbage collection and wear leveling, thereby classifying data, less frequently updated, into cold data and improving the performance of garbage collection and wear leveling.