Improving Compression Ratio, Area Overhead, and Test Application Time for System-on-a-Chip Test Data Compression/Decompression
Bibliographic record
Abstract
This paper proposes a new test data compression/decompression method for systems-on-a-chip. The method is based on analyzing the factors that influence test parameters: compression ratio, area overhead and test application time. To improve compression ratio, the new method is based on a Variable-length Input Huffman Coding (VIHC), which fully exploits the type and length of the patterns, as well as a novel mapping and reordering algorithm proposed in a pre-processing step. The new VIHC algorithm is combined with a novel parallel on-chip decoder that simultaneously leads to low test application time and low area overhead. It is shown that, unlike three previous approaches which reduce some test parameters at the expense of the others, the proposed method is capable of improving all the three parameters simultaneously. For example, the proposed method leads to similar or better compression ratio when compared to frequency directed run-length coding, however with lower area overhead and test application time. Similarly, there is comparable or lower area overhead and test application time with respect to Golomb coding , with improvements in compression ratio. Finally, there is similar or improved test application time when compared to selective coding, with reductions in compression ratio and significantly lower area overhead. An experimental comparison on benchmark circuits validates the proposed method.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".