Embedded system partitioning with flexible granularity by using a variant of tabu search
Why this work is in the frame
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Bibliographic record
Abstract
Various techniques to partition a system into hardware and software blocks have been proposed in the past. Most of these techniques use some form of control flow graphs (CFG) and employ optimization algorithms like simulated annealing or tabu search to reach an optimal solution. A partitioning method presented in this paper partitions a CFG representation by employing a variant of tabu search, which uses a dynamic tabu list. Fixed tabu list has been employed by most of the conventional algorithms. Our method works with a flexible level of granularity and it merges CFG nodes into partitioning objects under a defined set of rules. An initial partitioning object is selected and improved on subsequent iterations to find the best solution that satisfies the given constraints. The performance of the proposed method is compared with simulated annealing and conventional tabu search-based approaches that shows promising results.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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