Optimal application mapping to 2D-mesh NoCs by using a tabu-based particle swarm methodology
Bibliographic record
Abstract
A hybrid optimization scheme is presented in this paper that combines Tabu-search, Force Directed Swapping and Discrete Particle Swarm Optimization for Network-on-Chip (NoC) mapping problem. The main goal of the optimization is to map an application core graph such that the overall communication latency and energy consumption of the NoC are minimal. Discrete Particle Swarm Optimization is used as the main optimization scheme where each particle move is influenced by a force derived from the network traffic matrix. We also employ a Tabu-list to discourage swarm particles to re-visit the explored search space. This is done through particle reflection which proposes an alternative route towards the intended move direction. The methodology is tested for some multimedia application core graphs as well as randomly generated large network of synthetic cores. It was found that on average, this hybrid algorithm required less number of iterations to reach an optimal solution as compared to other existing and past algorithms without losing the quality of NoC mapping.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".