Exploration of Temperature Constraints for Thermal-Aware Mapping of 3D Networks-on-Chip
Bibliographic record
Abstract
This paper proposes three ILP-based static thermal-aware mapping algorithms for 3D Networks-on-Chip (NoC). With these three mapping algorithms, the authors explore the thermal constraints and their effects on temperature and performance. Through complexity analysis, the authors show that the first algorithm, an optimal one, is not suitable for 3D NoCs. Therefore, the authors develop two approximation algorithms and analyze their algorithmic complexities to show their proficiency. According to simulation results, mapping algorithms that employ direct thermal calculation to minimize the temperature reduce the peak temperature by up to 24% and 22%, for the benchmarks that have the highest communication rate and largest number of tasks, respectively. This peak temperature reduction comes at the price of a higher power-delay product. The authors’ exploration shows that considering power balancing early in the mapping algorithm does not affect chip temperature. Moreover, the authors show that considering explicit performance constraints in the thermal mapping has no major effect on performance.
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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 | 0.001 | 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 | 0.001 |
| 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".