Tracking Multicore Contention in Memory Controllers and DRAM
Bibliographic record
Abstract
The main memory subsystem has traditionally been one of the more complex resources to analyze in multicore real-time embedded systems, with memory controller considerations and JEDEC timing constraints being the more prominent factors contributing to such complexity. One of the main challenges in multicore real-time systems is the production of the necessary evidence regarding the management of contention for the certification of multicore platforms in safety-relevant sectors. As current MPSoC platforms provide little information on how tasks may be interacting and delaying each other at large, it still remains a tall order to provide evidence about the correctness of hardware and software mechanisms deployed specifically to mitigate and manage contention on shared resources. This work attempts to bridge this gap by proposing a low-overhead hardware mechanism to tightly track inter-core contention within the main memory subsystem. The proposed technique enhances the quality of timing- and contention-related evidence, increasing the explainability and management of multicore contention in the main memory subsystem for multicore real-time systems in relation to applicable safety standards regulating their usage.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".