A Low-Cost Fault-Tolerant Racetrack Cache Based on Data Compression
Bibliographic record
Abstract
SRAM-based cache memory faces several scalability limitations in deep nanoscale technologies, e.g., high leakage current, low cell stability, and low density. Emerging Non-Volatile Memory (NVM) technologies have received lots of attention in recent years, where Racetrack Memory (RTM) is among the most promising ones. RTM has the highest density among all NVMs and its access performance is comparable to SRAM technology. Therefore, RTM is a suitable alternative for SRAM in the Last-Level Caches (LLCs). Despite all its benefits, RTM confronts different reliability challenges due to the stochastic behavior of its storage element and highly error-prone data shifting, leading to a high probability of multiple-bit errors. Conventional Error-Correcting Codes (ECCs) are either incapable of tolerating multiple-bit errors or require a large amount of extra storage for check bits. This paper proposes taking advantage of value locality for compressing data blocks and freeing up a large fraction of cache blocks for storing data redundancy of strong ECCs. Utilizing the proposed scheme, a large majority of cache blocks are protected by strong ECCs to tolerate multiple-bit errors without any storage overhead. The evaluation using gem5 full-system simulator demonstrates that the proposed scheme enhances the mean-time-to-failure of the cache by an average of 11.3x with less than 1% hardware and performance overhead.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".