Buffer Space Allocation for Real-Time Priority-Aware Networks
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Bibliographic record
Abstract
In this work, we address the challenge of incorporating buffer space constraints in worst-case latency analysis for priority-aware networks. A priority-aware network is a wormhole-switched network-on-chip with distinct virtual channels per priority. Prior worst-case latency analyses assume that the routers have infinite buffer space allocated to the virtual channels. This assumption renders these analyses impractical when considering actual deployments. This is because an implementation of the priority-aware network imposes buffer constraints on the application. These constraints can result in back pressure on the communication, which the analyses must incorporate. Consequently, we extend a worst- case latency analysis for priority-aware networks to include buffer space constraints. We provide the theory for these extensions and prove their correctness. We experiment on a large set of synthetic benchmarks, and show that we can deploy applications on priority-aware networks with virtual channels of sizes as small as two flits. In addition, we propose a polynomial time buffer space allocation algorithm. This algorithm minimizes the buffer space required at the virtual channels while scheduling the application sets on the target priority-aware network. Our empirical evaluation shows that the proposed algorithm reduces buffer space requirements in the virtual channels by approximately 85% on average.
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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 | 0.000 | 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