Using GPUs to accelerate FPGA wirelength estimate for use with complex search operators
Bibliographic record
Abstract
As the precise wirelength for a given placement can only be known after routing, accurate and fast to compute wirelength estimates are required for FPGA placement algorithms. Two of the more effective wirelength estimation models are HPWL [1] and Star+ [2]. However, both of these models are expensive to compute requiring O(nm) time, where n is the number of nets and m is the average number of blocks to connect. In this paper, we show that the time to compute HPWL and Star+ can be reduced by as much as 577x and 548x, re spectively, by exploiting the computational power available in modern Graphical Processing Units (GPUs). To reduce the runtime required to compute HPWL and Star+ we propose a set of data structures targeted specifically for the GPU archi tecture. We then investigate five different mappings of these data structures to the GPU to determine which mapping best exploits the heterogeneous memories and thread-level parallelism available on the GPU. Though our results are geared towards FPGA placement, they extend naturally to the less constrained VLSI placement problem.
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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".