Alternatives in Designing Level-Restoring Buffers for Interconnection Networks in Field-Programmable Gate Arrays
Bibliographic record
Abstract
Programmable routing and logic in field-programmable gate arrays are implemented using nMOS pass transistors. Since the threshold voltage drop across an nMOS device degrades the high logic value, causing the pMOS transistor of the downstream buffer to not turn fully off, this approach suffers from static power consumption and reduced noise margins. The standard pMOS transistor pull-up in an active feedback of an inverter reduces the static power consumption, but degrades the switching time and/or active power consumption. We propose a circuit technique to build level-restoring buffers, which improves the propagation delay or active power consumption at a tiny area penalty. Our main idea is to replicate the nMOS element of the downstream buffer, where each replica is driven by a signal that originates from earlier stages of the nMOS-tree multiplexer. This way, when passing high logic values, signals from earlier stages directly drive the downstream buffer improving the delay or the slope of the transition edge. The passing of low logic values is still performed in the original way by the nMOS tree and the pMOS element of the downstream buffer. The simulations indicate an average improvement of the composite metric area-delay-energy product of 25% versus the standard approach across 180 nm, 130 nm, and 90 nm technologies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".