Analysis and design of low-power multi-threshold MCML
Bibliographic record
Abstract
Multi-threshold MOS current mode logic (MTMCML) is a natural evolution for MCML that offers power saving through supply voltage reduction while retaining the same performance. In this work, analytical formulation based on the BSIM3v3 model is proposed for MTMCML with error within 10% compared to HSPICE. The formulation helps designers to efficiently design MTMCML circuits without undergoing the time-consuming HSPICE simulations. Furthermore, it provides design guidelines and aids for designers to fully understand the different tradeoffs in MTMCML design. In addition, the analysis is extended to study the impact of technology scaling and parameter variations on MTMCML. It is shown that the worst case variation in the minimum supply voltage of MTMCML is 1.16%, thus suggesting maximal power saving.
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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 | 0.000 | 0.001 |
| 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".