Using link-level latency analysis for path selection for real-time communication on NoCs
Why this work is in the frame
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Bibliographic record
Abstract
We present a path selection algorithm that is used when deploying hard real-time traffic flows onto a chip-multiprocessor system. This chip-multiprocessor system uses a priority-based real-time network-on-chip interconnect between the multiple processors. The problem we address is the following: given a mapping of the tasks onto a chip-multiprocessor system, we need to determine the paths that the traffic flows take such that the flows meet there deadlines. Furthermore, we must ensure that the deadline is met even in the presence of direct and indirect interference from other flows sharing network links on the path. To achieve this, our algorithm utilizes a link-level analysis to determine the impact of a link being used by a flow, and its affect on other flows sharing the link. Our experimental results show that we can improve schedulability by about 8% and 15% over Minimum Interference Routing and Widest Shortest Path algorithms, respectively.
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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