Transistor grouping and metal layer trade-offs in automatic tile layout of FPGAs
Bibliographic record
Abstract
The physical layout of modern commercial FPGAs is one of the last bastions of manual VLSI layout. Our recent work has automated the FPGA layout process from architectural description to mask-level layout of the repeated FPGA tile. Here we improve on that work using two approaches: 1) by making better choices for the grouping of circuitry into the cells used for the layout and 2) through better allocation of metal. The new groupings improve the FPGA tile area by between 10% and 14%. That, together with the superior metal layer allocation allows us to automatically lay out a very accurate capture of a Xilinx Virtex-E tile that is only 54% to 92% larger than the real thing. With two additional metal layers, our tile area is only 13% larger. In addition, we show that a standard cell implementation is 102% larger than the real Xilinx Virtex-E.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".