Speeding Up FPGA Placement: Parallel Algorithms and Methods
Bibliographic record
Abstract
Placement of a large FPGA design now commonly requires several hours, significantly hindering designer productivity. Furthermore, FPGA capacity is growing faster than CPU speed, which will further increase placement time unless new approaches are found. Multi-core processors are now ubiquitous, however, and some recent processors also have hardware support for transactional memory (TM), making parallelism an increasingly attractive approach for speeding up placement. We investigate methods to parallelize the simulated annealing placement algorithm in VPR, which is widely used in FPGA research. We explore both algorithmic changes and the use of different parallel programming paradigms and hardware, including TM, thread-level speculation (TLS) and lock-free techniques. We find that hardware TM enables large speedups (8.1x on average), but compromises “move fairness” and leads to an unacceptable quality loss. TLS scales poorly, with a maximum 2.2x speedup, but preserves quality. A new dependency checking parallel strategy achieves the best balance: the deterministic version achieves 5.9x speedup and no quality loss, while the non-deterministic, lock-free version can scale to a 34x speedup.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".