Scalable analytic placement for FPGA on GPGPU
Bibliographic record
Abstract
The growth in field-programmable gate array (FPGA) capacity has outpaced improvements in serial processor speeds for the last decade and will continue for the foreseeable future. Unfortunately, as modern FPGAs have millions of logic elements and continue to grow, the compilation of designs can take hours or even days to complete. As a result, the runtimes of placement and routing flow have become a major concern for FPGA users and vendors alike. Roughly half the total compilation time is spent in the placement phase. Analytic placement algorithms solve the FPGA placement problem quickly. With an aim toward developing a scalable FPGA placement algorithm, we present a parallel analytic placement algorithm implemented on general-purpose computing graphics processing units (GPGPUs). The proposed analytic placer is scalable, that is, the placer maintains parallel efficiency as the problem size grows and number of parallel workers increase. Our algorithm is a parallelized version of the serial analytic placement algorithm StarPlace and achieves speedups of 13-31 times compared to this serial version. The proposed parallel algorithm is on average 78 times faster than the academic tool versatile place and route (VPR) when run in its fast, wirelength driven mode. The wirelength is on average 3% lower than VPR, with a 24% reduction in critical-path delay.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".