Temperature control in three‐network on chips using task migration
Bibliographic record
Abstract
Combination of three‐dimensional (3D) IC technology and network on chip (NoC) is an effective solution to increase system scalability and also alleviate the interconnect problem in large‐scale integrated circuits. However, because of the increased power density in 3D NoC systems and the destructive effect of high temperatures on chip reliability, applying thermal management solutions becomes crucial in such circuits. In this study, the authors propose a runtime distributed migration algorithm based on game theory to balance the heat dissipation among processing elements (PEs) in a 3D NoC chip multiprocessor. The objective of this algorithm is to minimise the 3D NoC system's peak temperature, as well as the overhead imposed on chip performance during migration. Owing to the high thermal correlation between adjacent PEs in the same stack in 3D NoCs, the authors model this multi‐objective problem as a cooperative game. The simulation results indicate upto 23 and 27% decrease in peak temperature, for the benchmarks that have the highest communication rate and the largest number of tasks, respectively. This comes at the price of slight migration overhead in terms of power‐delay product.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".