Post-silicon code coverage evaluation with reduced area overhead for functional verification of SoC
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".