Durable Address Translation in PCM-based Flash Storage Systems
Bibliographic record
Abstract
Phase change memory (PCM) is a promising DRAM alternative because of its non-volatility, high density, low standby power and close-to-DRAM performance. These features make PCM an attractive solution to optimize the management of NAND flash memory in embedded systems. However, PCM's limited write endurance hinders its application in embedded systems. Therefore, how to manage flash memory with PCM-particularly guarantee PCM a reasonable lifetime-becomes a challenging issue. In this paper, we propose to partially replace DRAM using PCM to optimize the management of flash memory metadata for better system reliability in the presence of power failure and system crash. To prolong PCM's lifetime, we present a write-activity-aware PCM-assisted flash memory management scheme, called PCM-FTL. By differentiating sequential and random I/O behaviors, a novel two-level mapping mechanism and a customized wear-leveling scheme are developed to reduce writes to PCM and extend its lifetime. We evaluate PCM-FTL with a variety of general-purpose and mobile I/O workloads. Experimental results show that PCM-FTL can significantly reduce write activities and achieve an even distribution of writes in PCM with very low overhead.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".