Power-aware Mapping for 3D-NoC Designs Using Genetic Algorithms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Scalable 3D Networks-on-Chip (NoC) designs are needed to match the ever-increasing communication and low-power demands of large-scale multi-core applications. However, chip designers do not have the necessary tools to implement their applications efficiently at different layers of the design hierarchy. A design methodology for low power 3D-NoCs applications is needed to achieve the best performance. To address this problem, we use Genetic Algorithms to find the best 3D-NoC mesh network mapping that achieves minimum power consumption for a given application. As a proof of concept, a case study of a multicore application that has 32 symmetric microprocessors is presented. We used Genetic Algorithms to calculate the fitness function and solve the optimization problem in less than four minutes, whereas it took over three days using exhaustive search and yet to find the minimum power consumption.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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