A reliability-aware design methodology for Networks-on-Chip applications
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Bibliographic record
Abstract
Network reliability is a key design issue that impacts the performance of all Networks-on-Chip-based systems. In this paper, we develop two reliability models for on-chip interconnection networks using both deterministic and probabilistic measures. Graph-theoretic concepts are adopted with modifications to obtain application-specific reliability models for nine regular network topologies. Using these models, a new methodology is proposed to improve the network reliability of any target application using a topology-based design approach. To validate the effectiveness of the proposed methodology, a case study was performed using an MPEG4 video application. The results were promising and proved that the proposed methodology helps designers better evaluate the impact of their network architecture on the system reliability and assists them in choosing the most appropriate architecture for a target application at early design phases.
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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 | 0.001 | 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