A Software-Based Method for Test Vector Compression in Testing System-on-a-Chip
Bibliographic record
Abstract
A new software-based hybrid test vector compression method for testing system-on-a-chip (SOC) using an embedded processor is presented in this paper. In the proposed approach, a software program is first loaded into the on-chip processor memory core together with the compressed test data set. In order to reduce on-chip storage as well as testing time, the large volume of test data input is compressed in a hybrid fashion before being downloaded into the processor. The method combines a set of adaptive coding techniques for the required test data compression. The compression program, however, need not be loaded into the embedded processor, since only the decompression of test data is necessary for application by the automatic test equipment (ATE). Most importantly, this software-based hybrid scheme requires minimal hardware overhead, while the on-chip embedded processor core can be reused for normal operation after the testing is completed. In the paper, only the compression part of the technique is presented, and the efficiency of the suggested hybrid approach is demonstrated through simulation experiments on ISCAS 85 combinational and ISCAS 89 full-scan sequential benchmark circuits
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".