Block Bitmap Mapping for Faster Memory Garbage Collection
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Solution Overview
Problem
Evaluating an entire logical-to-physical (L2P) table to identify valid data during garbage collection operations in memory systems is inefficient due to the large size of the table, leading to significant latency and overhead.
Innovation Solution
Implementing a bitmap for each block of memory cells to indicate relevant subsets of the L2P table, allowing the memory system to efficiently identify valid data by evaluating only these subsets instead of the entire table, with configurable subset sizes to adjust overhead and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire L2P table is evaluated to identify valid data during garbage collection, then all valid data can be accurately identified, but the operation latency increases significantly and overhead becomes unmanageable
Solution Approach 1:
The L2P table is divided into multiple subsets, each associated with a specific block of memory cells. Instead of evaluating the entire L2P table during garbage collection, the system only evaluates the relevant subset corresponding to the target block, dramatically reducing evaluation time and latency while maintaining accurate identification of valid data within that block.
2Reliability
If the entire L2P table is evaluated to identify valid data, then complete data validation is achieved, but the computational overhead becomes excessive and unmanageable
Solution Approach 1:
The L2P table is segmented into multiple subsets that can be independently evaluated. This segmentation allows the garbage collection process to focus only on the relevant subset for each block, reducing computational overhead and complexity while maintaining complete validation of data within the scope of each block through selective evaluation.
3Loss of time
If subset size is reduced to minimize L2P table evaluation, then garbage collection latency decreases, but the number of subsets increases and bitmap complexity grows
Solution Approach 1:
The system dynamically adjusts the granularity of L2P table subsets based on operational requirements. By making subset size configurable, the system can optimize the balance between garbage collection latency and bitmap complexity depending on the specific use case, allowing flexible tuning of performance parameters without being constrained by a fixed structure.
Data Source
AI summary
Methods, systems, and devices for valid data identification for garbage collection are described. In connection with writing data to a block of memory cells, a memory system may identify a portion of a logical address space that includes a logical address for the data. The memory system may set a bit of a bitmap, which may indicate that the block includes data having a logical address within a portion of the logical address space corresponding to the bit. The logical address space may be divided into any quantity of portions, each corresponding to a different subset of a logical-to-physical (L2P) table, and the bitmap may include any quantity of corresponding bits. To perform garbage collection on the block, the bitmap may be used to identify one or more subsets of the L2P table to evaluate to determine whether different sets of data within the block are valid or invalid.