Timing Analysis and Tracing Concepts for ECU Development
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Integration scenarios for ECU software become more complicated, as more constraints with regards to timing, safety and security need to be considered. Multi-core microcontrollers offer even more hardware potential for integration scenarios. To tackle the complexity, more and more model based approaches are used.</div><div class="htmlview paragraph">Understanding the interaction between the different software components, not only from a functional but also from a timing view, is a key success factor for high integration scenarios.</div><div class="htmlview paragraph">In particular for multi-core systems, an amazing amount of timing data can be generated. Usually a multi-core system handles more software functionality than a single-core system. Furthermore, there may be timing interference on the multicore systems, due to the shared usage of buses, memory banks or other hardware resources. The current approach for timing analysis, often based on execution times and sequences of executions in Gantt charts, will not scale arbitrarily for high integration scenarios on multi-core systems.</div><div class="htmlview paragraph">This report introduces hardware support for non-intrusive tracing and a software analysis methodology with a focus on the analysis of multi-core software. Hence, race conditions and performance issues, often caused by access conflicts with buses or memories, can be investigated efficiently. Background for the constraints of non-intrusive tracing support on modern cost sensitive microcontrollers is given. Hardware support for non-intrusive tracing is not a new technology; it exists for several microcontrollers since years. However, it gains a lot of importance for multi-core systems. The developer needs to consider the complete system including buses and memories.</div></div>
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".