Improving Networks‐on‐Chip performability: A topology‐based approach
Bibliographic record
Abstract
Abstract The performability metric is commonly used in Networks‐on‐Chip (NoC)‐based systems to represent their abilities to successfully complete specific tasks in finite time intervals. In this paper, we present a novel topology‐based performability model for NoC‐based systems. The model is used to evaluate the performability of NoC‐based systems at early design phases. A comparative study of nine commonly used network architectures is performed using the proposed model. The purpose of the study is to explore the impact of the network topology on the performability of NoC‐based systems. Using the output from this study, a new methodology is proposed to improve the performability of a given application at early design phases. In this methodology, a joint consideration of five design parameters (network topology, target application traffic distribution, mapping of processing elements, noise power, and voltage swing) is carried out. Using the proposed methodology, designers can select the optimal topology for a given application that maximizes system performability. The effectiveness of the proposed methodology in determining the optimal topology is verified by experimental work and validated through a case study of a video application. Copyright © 2010 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".