Towards scalable placement for FPGAs
Bibliographic record
Abstract
Placement based on simulated annealing is in dominant use in the FPGA community due to its superior quality of result (QoR). However, given the progression of FPGA device capacity to the order of 100K LUTs, the long runtime associated with simulated annealing warrants a revisit of other placement paradigms in the context of FPGAs. In this paper, we attempt to make a rigorous comparison of a recent crop of academic ASIC placers and VPR when applied to modern FPGA device features and design sizes. We also report a new detailed placer, MDP, based on a new problem formulation of maximum-bipartite matching. We show that MDP is 3X to 7X faster than the detailed placer in FastPlace, which until now has been the fastest detailed placer publicly available. Furthermore, this speedup occurs while producing comparable or superior QoR. With these results, we speculate promising research directions towards scalable, high quality FPGA placement flows that can change the user experience from an overnight wait-time to a coffee break wait-time -- even on large benchmarks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".