Replace: An incremental placement algorithm for field programmable gate arrays
Bibliographic record
Abstract
Recompiling a large circuit after making a few logic changes is a time-consuming process. We present an incremental placement algorithm for FPGAs that is focused on extremely fast runtime for changes which can be localized. It is capable of handling multiple changes across large regions of an FPGA. This is especially useful when used with a floorplan where a modified subcircuit is instantiated several times in the design hierarchy or where several subcircuits are modified. The algorithm is simpler and faster than past approaches because its insertion and legalization steps are based on CPU-efficient shifting steps which do not continuously evaluate the impact of each move on costs. Instead, any lost quality is recovered by a fast, low-temperature anneal at the end. When 35,000 out of 50,000 LUTs are modified, the incremental placement (including fast anneal) is 7 times faster than VPR's "fast placement" from scratch with only 2% quality degradation. The key concepts utilized in the incremental placement algorithm include uses of floor-planning constraints, CPU-efficient CLB shifting, super placement grid and a tuned annealing refinement process.
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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".