Multi-objective Tabu Search based topology generation technique for application-specific Network-on-Chip architectures
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
This paper presents a power and performance multi-objective Tabu Search based technique for designing application-specific Network-on-Chip architectures. The topology generation approach uses an automated technique to incorporate floorplan information and attain accurate values for wirelength and area. The method also takes dynamic effects such as contention into account, allowing performance constraints to be incorporated during topology synthesis. A new method for contention analysis is presented in this work which makes use of power and performance objectives using a Layered Queuing Network (LQN) contention model. The contention model is able to analyze rendezvous interactions between NoC components and alleviate potential bottleneck points within the system. Several experiments are conducted on various SoC benchmark applications and compared to previous works.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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