Integrated test data decompression and core wrapper design for low-cost system-on-a-chip testing
Bibliographic record
Abstract
This paper discusses an integrated solution for reducing the volume of test data for deterministic system-on-a-chip testing. The proposed solution is based on a new test data decompression architecture which exploits the features of a core wrapper design algorithm targeting the elimination of useless test data. The compressed test data can be transferred from the automatic test equipment to the on-chip decompression architecture using only one test pin, thus providing an efficient reduced pin count test methodology for multiple scan chains-based embedded cores. In addition to reducing the volume of test data, the proposed solution decreases the control overhead, test application time and power dissipation during scan. Further, it also requires lower on-chip area when compared to the testing scenarios which employ decompression architectures for every scan chain and it eliminates the synchronization overhead between the automatic test equipment and the system-on-a-chip. Moreover, the proposed solution is scalable and programmable and, since it can be considered as an add-on to a test access mechanism of a given width, it provides seamless integration with any design flow. Thus, the proposed integrated solution is an efficient low-cost test methodology for systems-on-a-chip.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".