Lazy Load Scheduling for Mixed-criticality Applications in Heterogeneous MPSoCs
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Bibliographic record
Abstract
Newly emerging multiprocessor system-on-a-chip (MPSoC) platforms provide hard processing cores with programmable logic (PL) for high-performance computing applications. In this article, we take a deep look into these commercially available heterogeneous platforms and show how to design mixed-criticality applications such that different processing components can be isolated to avoid contention on the shared resources such as last-level cache and main memory. Our approach involves software/hardware co-design to achieve isolation between the different criticality domains. At the hardware level, we use a scratchpad memory (SPM) with dedicated interfaces inside the PL to avoid conflicts in the main memory. At the software level, we employ a hypervisor to support cache-coloring such that conflicts at the shared L2 cache can be avoided. In order to move the tasks in/out of the SPM memory, we rely on a DMA engine and propose a new CPU-DMA co-scheduling policy, called Lazy Load , for which we also derive the response time analysis. The results of a case study on image processing demonstrate that the contention on the shared memory subsystem can be avoided when running with our proposed architecture. Moreover, comprehensive schedulability evaluations show that the newly proposed Lazy Load policy outperforms the existing CPU-DMA scheduling approaches and is effective in mitigating the main memory interference in our proposed architecture.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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