Hybrid Prototyping of Multicore Embedded Systems
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a novel modeling technique for multicore embedded systems, called Hybrid Prototyping. The fundamental idea is to simulate a design with multiple cores by creating an emulation kernel in software on top of a single physical instance of the core. The emulation kernel switches between tasks mapped to different cores and manages the logical simulation times of the individual cores. As a result, we can achieve fast and cycle-accurate simulation of symmetric multicore designs, thereby overcoming the accuracy concerns of virtual prototyping and the scalability issues of physical prototyping. Our experiments with industrial multicore designs show that the simulation time with hybrid prototyping grows only linearly with the number of cores and the inter-core communication traffic, while providing 100% cycle accuracy.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.006 |
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