On Optimizing Die-stacked DRAM Caches
Bibliographic record
Abstract
Die-stacking is a new technology that allows multiple integrated circuits to be stacked on top of each other while connected with a high-bandwidth and high-speed interconnect. In particular, die-stacking can be useful in boosting the effective bandwidth and speed of DRAM systems. Die-stacked DRAM caches have recently emerged as one of the top applications of die-stacking. They provide higher capacity than their SRAM counterparts and are faster than offchip DRAMs. In addition, DRAM caches can provide almost eight times the bandwidth of off-chip DRAMs. They, however, come with their own challenges. Since they are only twice as fast as main memory, they considerably increase latency for misses and incur significant energy overhead for remote lookups in snoop-based multi-socket systems. In this thesis, we present a Dual-Grain Filter for avoiding unnecessary accesses to the DRAM cache at reduced hardware cost and we compare it to recent works on die-stacked DRAM caches.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".