Impact of technology scaling on leakage reduction techniques
Bibliographic record
Abstract
Techniques that reduce total leakage in circuits operating in the active mode at different temperature conditions are examined. Also, the implications of technology scaling on the choice of techniques to mitigate total leakage are investigated. Logic gates in the 65 nm, 45 nm, and 32 nm nodes are simulated and analyzed. The techniques that are adopted for comparison in this work affect both gate and subthreshold leakage, namely, stack forcing, pin reordering, reverse body biasing, and high threshold voltage transistors. Aside from leakage, our analysis also highlights the impact of these techniques on the circuit's performance and noise margins. Power sensitive technology mapping tools can use the guidelines found in this work in the low power design flow, to meet the required maximum leakage current in a circuit. These guidelines are presented in general terms so that they can be adopted for any application and process technology.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".