KBMA: A knowledge‐based multi‐objective application mapping approach for 3D NoC
Why this work is in the frame
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Bibliographic record
Abstract
Due to increased demands for communication at low power, an efficient application mapping has become vital in the area of network on chip (NoC). Optimisation of architectural structure in on‐chip design is essential to maximise the performance of the network and minimise the cost functions. To address this issue, a knowledge‐based memetic algorithm (KBMA) is proposed for 3D NoC for successful mapping with standard network topologies. The proposed KBMA adopts power, area and delay as a cost function for an effective mapping. The competence of the proposed method is verified through comparison with other natural inspired algorithms like particle swarm optimisation and genetic algorithm. The presented work is validated through four case studies which include real application benchmarks of NoC and random generated benchmarks using test graph for free.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 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