A scalable, serially-equivalent, high-quality parallel placement methodology suitable for modern multicore and GPU architectures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Placement and routing run-times continue to dominate the automated FPGA design flow. As the size of FPGA architectures continue to grow exponentially, it remains critical to develop parallel tools for FPGA design where the amount of exposed concurrent work scales with the size of the designs to be synthesized. In this paper, we propose a novel algorithm for parallel placement, based on simulated annealing, where the amount of parallel work directly scales with the size of the net-list to be placed. Our approach concurrently evaluates and conditionally applies very large sets of non-conflicting swaps using common parallel computing primitives, including stream compaction, category reduction, and sort. While our design is suitable for targeting all modern parallel computing platforms, we present results from our implementation which targets NVIDIA's CUDA platform, where we achieve a mean speed-up of 19x over VPR with post-routing critical-path-delay and wire-length quality that matches or exceeds VPR. We believe that this work is an important step towards the development of a scalable, high-quality placement tool.
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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 | 0.001 | 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 it