Bibliographic record
Abstract
This thesis proposes and evaluates Tagfilter, a novel power-aware cache hierarchy, coupled with an alternate cache indexing scheme, as a means for reducing power dissipation in high-level caches by filtering out unnecessary accesses. As high-level caches get larger and increase in associativity, they will inevitably consume sizable amounts of power. Interposed between the L1 and the L2 tag arrays, Tagfilter is a small, cache-like structure that caches a small number of recently accessed L2 tag sets, and filters out unnecessary tag array accesses that would otherwise waste considerable power in the L2 tag array. The Tagfilter exploits the tag-set locality existing in typical L2 access patterns. We also propose an alternate cache indexing scheme, which increases Tagfilter's efficiency (filter rate) by increasing tag-set locality. The Tagfilter results in reduced power because it reduces accesses to the much more power-demanding L2 tag array. Power savings by the Tagfilter are evaluated using the SPEC CPU2000 benchmarks running on SimpleScalar V3.0, a software processor model. The modified Wattch power models are used. On average, the Tagfilter reduces the power dissipation in the L2 tag array by 43% for the writeback L1 data cache and by 85% for the writethrough L1 data cache.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".