Designing zero-aliasing space compressors: graph theory approach
Bibliographic record
Abstract
The design of space-efficient support hardware for builtin self-testing (BIST) is of great significance in the synthesis of VLSI circuits. An approach based on graph theory to designing zero-aliasing space compression hardware for single stuck-line faults is proposed in this paper, extending some of the well-known concepts in switching theory, specifically the notion of compatibility relation as employed in the minimization of incomplete sequential machines, based on optimal generalized sequence mergeability, developed and utilized by the authors in previous works. The suggested compaction technique possesses several advantages over earlier ones, viz. zero-aliasing is achieved here without any modification of the module under test (MUT), and the area overhead and signal propagation delay are relatively low. Besides, the method is suitable for application with both deterministic compacted and pseudorandom test vectors. The paper furnishes details of the algorithms required in the implementation, based on the criteria of merger for an optimal number of outputs of the MUT to realize maximal compaction in the design, along with results of experiments conducted on ISCAS 85 combinational benchmark circuits, with simulation programs ATALANTA and FSIM.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".