Triple-Threshold Static Power Minimization Technique in High-Level Synthesis for Designing High-Speed Low-Power SOC Applications Using 90nm MTCMOS Technology
Bibliographic record
Abstract
This paper reports a novel triple-threshold static power minimization technique in high-level synthesis of highspeed low-power SOC applications. Using 90 nm multi-threshold CMOS (MTCMOS) technology, we evaluate the performance and power dissipation of benchmark circuits synthesized using transistors with different threshold voltages. Using static timing analysis, we determine the timing requirements of cells and place cells with low and standard threshold voltages in the critical paths. Cells with a high threshold voltage are placed in non-critical paths to minimize the static power with no overall timing degradation. From the timing and power analysis, we determine the optimal placement of high, standard and low threshold voltage cells. Applying the new triple-threshold technique to optimize 20 circuits originating from the ISCAS'99 benchmark, we have achieved an average saving of 85.3% in the static power compared to conventional all-LVT circuits, and 39.6% saving compared to the dual-threshold (HVT+LVT) technique.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".