Unified multi‐objective mapping and architecture customisation of networks‐on‐chip
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
One of the challenging problems in networks‐on‐chip (NoC) design is optimising the architectural structure of the on‐chip network in order to maximise the network performance while minimising the corresponding costs. In this study, a methodology for multi‐objective optimisation of NoC standard architectures using Genetic Algorithms is presented. The methodology considers two cost metrics, power and area, and two performance metrics, delay and reliability. Our methodology combines the best selection of NoC standard topology, the optimum mapping of application cores onto that topology, and the best routing of application traffic traces over the generated network. The methodology is evaluated by applying it to different NoC benchmark applications as case studies. Results show that the architectures generated by our methodology outperform those of other standard architecture customisation techniques with respect to four metrics: power, area, delay and reliability, and their combination.
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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 | 0.000 | 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 | 0.000 | 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 it