Post-silicon code coverage evaluation with reduced area overhead for functional verification of SoC
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Effective techniques for post-silicon validation are required to better evaluate functional correctness of increasingly complex SoCs. Coverage is the standard measure of validation effectiveness and is extensively used pre-silicon. However, there is little data evaluating the coverage of post-silicon validation efforts on industrial-scale designs. In this paper, we address this knowledge gap. We have developed an industrial-size SoC, based entirely on open-source IP: roughly a “netbook-on-a-chip”, synthesizable to FPGA, and capable of running Linux, X11, and application software. This platform allows us to instrument the hardware to measure true post-silicon coverage achieved by typical post-silicon validation tests, such as booting the OS - tests that are impossibly expensive to run in pre-silicon simulation. Thus, we can compare coverage achieved pre - and post-silicon, and also measure the area overhead required to monitor post-silicon coverage. In addition, we apply state-of-the-art software analysis techniques to reduce the instrumentation overhead for coverage monitoring. Our results show: (1) The typical test of booting the OS often achieves high coverage, well correlated to what is achieved by pre-silicon directed tests, but in some blocks the coverage can be markedly different, highlighting the importance of post-silicon validation in general and post-silicon coverage measurement in particular. (2) The area overhead of the coverage monitoring instrumentation is high, ranging from 1% to 22%. (3) State-of-the-art software analysis techniques reduce the overhead (e.g., nearly a 30% reduction for one block we instrumented), but the remaining overhead is still unacceptably high for practical deployment. Taken together, our results provide a solid baseline for further research on post-silicon coverage and test generation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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