DAMA: A Dual Arbitration Mechanism for Mixed-Criticality Applications
Why this work is in the frame
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Bibliographic record
Abstract
We discuss hardware resource management in mixed-criticality systems, where requestors may issue latency-critical (LTC) and non-latency-critical (NLTC) requests. LTC requests must adhere to strict latency bounds imposed by safety-critical applications, but timely servicing NLTC requests is necessary to maximize overall system performance in the average case. In this paper, we address this tradeoff for a shared memory resource by proposing DAMA, a dual arbitration mechanism that imposes an upper bound on the cumulative latency of LTC requests without unduly impacting NLTC performance. DAMA comprises a high-performance arbiter, a real-time arbiter, and a mechanism that constantly monitors the cumulative latency of requests suffered by each requestor. DAMA primarily executes in high-performance mode and only switches to real-time mode in the rare instances when its incorporated mechanism detects a violation of a task’s timing guarantee. We demonstrate the effectiveness of our arbitration scheme by adapting a predictable prefetcher that issues NLTC requests and attaching it to the L1 caches of our cores. We show both formally and experimentally that DAMA provides timing guarantees for LTC requests while processing other NLTC requests. We also demonstrate that with a negligible overhead of less than 1.5% on the cumulative latency bound of LTC requests, DAMA can achieve an equivalent average performance to a prefetcher that processes requests under a high-performance arbitration scheme.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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