Timing driven gate duplication
Bibliographic record
Abstract
In the past few years, gate duplication has been studied as a strategy for cutset minimization in partitioning problems. This paper addresses the problem of delay optimization by gate duplication. We present an algorithm to solve the gate duplication problem. It traverses the network from primary outputs(PO) to primary inputs(PI) in topologically sorted order evaluating tuples at the input pins of gates. The tuple's first component corresponds to the input pin required time if that gate is not duplicated. The second component corresponds to the input pin required time if that gate were duplicated. After tuple evaluation the algorithm traverses the network from PI to PO in topologically sorted order, deciding the gates to be duplicated. The last and final traversal is again from PO to PI, in which the gates are physically duplicated. Our algorithm uses the dynamic programming structure. We report delay improvements over other optimization methodologies. Gate duplication, along with other optimization strategies, can be used for meeting the stringent delay constraints in today's ultra complex designs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".