A Comparative Study Between Static and Dynamic Sleep Signal Generation Techniques for Leakage Tolerant Designs
Bibliographic record
Abstract
Power gating techniques are rapidly gaining popularity assisting the management of leakage power consumption for deep submicrometer microprocessors' functional units. Power gating is based on an input sleep signal to set the functional unit into a low leakage mode. However, power gating techniques in general inherently lack information about the utilization profile of the functional units they manage. This limitation is usually handled either statically by using a fixed length counter that generates the sleep signal when the functional unit is idle for a specified number of cycles or dynamically by changing the number of cycles before the sleep signal is generated depending on the previous history of operation. In this paper, a comparative study between the static and dynamic approaches regarding the power-performance tradeoff will be presented. It will be shown that the dynamic sleep signal generator is capable of tracking the operation of the functional units while achieving accuracies up to 90% compared to an average of 40%-60% for the static sleep signal generator (SSSG). Additionally it saves up to 80% more leakage versus the SSSG. This study is very important in assisting circuit designers choose between both techniques depending on the power gated circuit.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 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".