Dynamic Standby Prediction for Leakage Tolerant Microprocessor Functional Units
Bibliographic record
Abstract
Leakage power is projected to comprise approximately 50% of the processor's power for sub 65 nm technologies. Much of this power is consumed in the processor's functional units. Accordingly, leakage control techniques are employed to reduce leakage in these functional units. Many of these techniques are dynamic and are based on an input sleep signal to initiate a low leakage mode. However, since most of these leakage control techniques are based on circuit level schemes, such techniques inherently lack information about the operational profile of the functional units they manage. This limitation is usually handled statically by using a fixed length counter that generates the sleep signal when the functional unit is idle for a specified number of cycles. In this paper, the limitations of the static sleep signal generation approach are identified, and the use of a dynamic alternative that is capable of adopting the counter length to the running application is proposed. In order to assess the accuracy of the proposed dynamic sleep signal generator, the length of the sleep period following the sleep signal generation is used as a metric to identify the usefulness of utilizing the dynamic approach. Experimental results for the dynamic alternative shows up to 98% accuracy in predicting the length of the standby period compared to an average of 40-60% in the static case, which translates into increased leakage savings. This is achieved while consuming 360 muW of overhead power at 1 GHz
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".