A Machine Learning Approach for Accelerating SimPL-Based Global Placement for FPGA's
Bibliographic record
Abstract
Many commercial FPGA placement tools are based on the SimPL framework where the Lower Bound (LB) phase optimizes wire length and timing without considering cell overlaps and the Upper Bound (UB) phase spreads out cells while considering the target FPGA architectures. In the SimPL framework, the number of iterations depends on design complexity and the quality of UB placement, which highly impacts runtime. In this work, we propose a machine learning (ML) scheme where the anchor weights of cells are dynamically adjusted to make the process converge in a pre-determined budget for the number of iterations. In our approach and for a given FPGA architecture, a ML model constructs a trajectory guide function that is used for adjusting anchor weights during SimPL's iterations. Our experimental results on industrial benchmarks show, we can achieve on average 28.01% and 4.7% runtime reduction in the runtime of Global Placement and the runtime of the whole placer, respectively while maintaining the quality of solutions within an acceptable range.
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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".