Fault-tolerance in VLSI systems design using data compression under constraints of failure probabilities-overview and current status
Bibliographic record
Abstract
The design of space-efficient support hardware for built-in self-testing (BIST) is of immense significance in the synthesis of present day very large-scale integration (VLSI) circuits and systems, particularly in the context of design paradigm shift from system-on-board to system-on-chip (SOC). This paper presents an overview of the general problem of designing zero-aliasing or aliasing-free space compression hardware in relation to embedded cores-based SOC for single stuck-line faults in particular, extending the well-known concepts of conventional switching theory, and of incompatibility relation to generate maximal compatibility classes (MCCs) utilizing graph theory concepts, based on optimal generalized sequence mergeability, as developed by the authors in earlier works. The paper briefly presents the mathematical basis of selection criteria for merger of an optimal number of outputs of the module under test (MUT) for realizing maximum compaction ratio in the design, along with extensive simulation results on International Symposium on Circuits and Systems or ISCAS 85 combinational and ISCAS 89 full-scan sequential benchmark circuits, with simulation programs ATALANTA, FSIM, and COMPACTEST.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".