Application Specific Low Leakage data Cache for embedded processors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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..
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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 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it