An Evaluation on the Accuracy of the Minimum-Width Transistor Area Models in Ranking the Layout Area of FPGA Architectures
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Bibliographic record
Abstract
This work provides an evaluation on the accuracy of the minimum-width transistor area models in ranking the actual layout area of FPGA architectures. Both the original VPR area model and the new COFFE area model are compared against the actual layouts with up to three metal layers for the various FPGA building blocks. We found that both models have significant variations with respect to the accuracy of their predictions across the building blocks. In particular, the original VPR model overestimates the layout area of larger buffers, full adders, and multiplexers by as much as 38%, while they underestimate the layout area of smaller buffers and multiplexers by as much as 58%, for an overall prediction error variation of 96%. The newer COFFE model also significantly overestimates the layout area of full adders by 13% and underestimates the layout area of multiplexers by a maximum of 60% for a prediction error variation of 73%. Such variations are particularly significant considering sensitivity analyses are not routinely performed in FPGA architectural studies. Our results suggest that such analyses are extremely important in studies that employ the minimum-width area models so the tolerance of the architectural conclusions against the prediction error variations can be quantified. Furthermore, an open-source version of the layouts of the actual FPGA building blocks should be created so their actual layout area can be used to achieve a highly accurate ranking of the implementation area of FPGA architectures built upon these layouts.
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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 | 0.001 | 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 | 0.001 | 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 it