Application Specific Low Leakage data Cache for embedded processors
Bibliographic record
Abstract
Previous studies have suggested using drowsy caches to reduce leakage power in caches. Such studies often move an entire cache line in and out of the drowsy mode to reduce leakage power while maintaining performance. In this work we extend previous work and introduce Application Specific Low Leakage Cache (ASL) as an alternative power-aware data cache for embedded processors. ASL builds on the observation that often only one or two words of a cache line are accessed during long periods. Accordingly, we investigate a word-size granularity approach to drowsy caches. We introduce two ASL variations, i.e., B-ASL and P-ASL. In B-ASL we move all words in a cache line into the drowsy (low leakage) mode and wakeup only the words accessed. In P-ASL we make sure recently accessed words stay in the non-drowsy (high leakage) mode to maintain performance. We show that ASL can reduce leakage power in caches by 88% while paying an average performance cost of 0.7% compared to drowsy cache..
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".