A Graph-Based I/O Pad Pre-placement Technique for Use with Analytic FPGA Placement Methods
Bibliographic record
Abstract
Typical analytic placement methods seek to minimize total squared wirelength by solving a linear equation system. However, to avoid trivial solutions, certain blocks must be assigned locations on the Field Programmable Gate Array (FPGA) fabric prior to optimization. A simple way to achieve this is to assign blocks randomly. However, this does not always result in the best solution. In this paper, we present a novel algorithm, called ShrubPlace, for pre-assigning I/O blocks to I/O pads around the perimeter of the FPGA. To verify the efficacy of our pre-placement algorithm, we integrated the algorithm into the analytic placer. When tested with the 20 MCNC benchmarks, our results show a reduction in wirelength is possible, with very little additional execution time required to perform the pre-placement.
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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 | 0.001 | 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".