Gate sizing and buffer insertion for optimizing performance in power constrained BiCMOS circuits
Bibliographic record
Abstract
This paper presents a method for optimizing BiCMOS logic networks that exploits the fact that such networks may use a mixture of both CMOS and BiCMOS gates. The method assumes a given network architecture and finds both the logic family and size for each gate so that total delay (power) is minimized subject to a power (delay) constraint. The method views a BiCMOS gate as a type of buffered CMOS gate and selects the logic family for each gate based on a sequence of gate/buffer sizing optimizations each formulated as a polynomial program. Thus, a high drive BiCMOS gate with a low fan-out can be identified and replaced with a lower power CMOS gate. For a 0.8 /spl mu/m BiCMOS process, an optimized mixed CMOS/BiCMOS 8-bit adder (8 /spl times/ 8 bit multiplier) is found to be up to 21% (17%) faster than the optimized CMOS version dissipating the same power.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".