Path Selection for Real-Time Communication on Priority-Aware NoCs
Bibliographic record
Abstract
This work investigates selecting paths for communication flows when deploying a hard real-time application on a chip-multiprocessor system. This chip-multiprocessor system uses a priority-aware real-time network-on-chip interconnect between the processors. Given a mapping of the computation tasks onto the chip-multiprocessor, the problem we address in this work is to discover paths the communication flows take such that hard real-time deadlines of flows are met. Furthermore, we must ensure that deadlines are 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 stage-level analysis for real-time communication to determine the impact of a network link being used by a flow, and its effect on other flows sharing the link. The path selection algorithm uses heuristics such as selecting links with least interference, and considering lower-priority flows when dedicating links to paths of higher-priority flows since an optimal one is intractable. The algorithm also considers constraints on the number of virtual channels at each router port in the network. The statistically significant experimental results show an improvement in schedulability by 5% and 12% over existing path selection algorithms such as Minimum Interference Routing and Widest Shortest Path algorithms, respectively. We also present a set-top box case study to further illustrate the benefits of using the proposed algorithm.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".