Analyzing System-Level Information’s Correlation to FPGA Placement
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Bibliographic record
Abstract
One popular placement algorithms for Field-Programmable Gate Arrays (FPGAs) is called Simulated Annealing (SA). This algorithm tries to create a good quality placement from a flattened design that no longer contains any high-level information related to the original design hierarchy. Placement is an NP-hard problem, and as the size and complexity of designs implemented on FPGAs increases, SA does not scale well to find good solutions in a timely fashion. In this article, we investigate if system-level information can be reconstructed from a flattened netlist and evaluate how that information is realized in terms of its locality in the final placement. If there is a strong relationship between good quality placements and system-level information, then it may be possible to divide a large design into smaller components and improve the time needed to create a good quality placement. Our preliminary results suggest that the locality property of the information embedded in the system-level HDL structure (i.e. “module”, “always”, and “if” statements) is greatly affected by designer HDL coding style. Therefore, a reconstructive algorithm, called Affinity Propagation, is also considered as a possible method of generating a meaningful coarse-grain picture of the design.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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 it