Bibliographic record
Abstract
Pass-transistors have been the key building block for field-programmable gate array (FPGA) circuitry for many years due to the very small switch they enable. However, passtransistor performance and reliability have been degrading with technology scaling. Transmission gates are an alternative to pass-transistors; while larger, they are more robust. We develop a new FPGA circuit optimization flow and use it to investigate the area, delay and power impact of building FPGAs out of transmission gates instead of pass-transistors in a 22nm process. Our results show that transmission gate FPGAs are 15% larger than pass-transistor FPGAs but are 10-25% faster depending on the allowable level of “gate boosting”. Without gate boosting, transmission gate FPGAs are the better option with 14% lower area-delay product. If 200mV of gate boosting is possible however, pass-transistor FPGAs remain the slightly better choice with a 2% better area-delay product. We also show that transmission gates with a separate power supply for their gate terminal enable a low-voltage FPGA with 50% less power and good delay.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".