Fault tolerance in systems design in VLSI using data compression under constraints of failure probabilities
Why this work is in the frame
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Bibliographic record
Abstract
The design of space-efficient support hardware for built-in self-testing (BIST) is of critical importance in the design and manufacture of VLSI circuits. This paper reports new space compression techniques which facilitate designing such circuits using compact test sets, with the primary objective of minimizing the storage requirements for the circuit under test (CUT) while maintaining the fault coverage information. The compaction techniques utilize the concepts of Hamming distance, sequence weights, and derived sequences in conjunction with the probabilities of error occurrence in the selection of specific gates for merger of a pair of output bit streams from the CUT. The outputs of the space compactor may eventually be fed into a time compactor (viz. syndrome counter) to derive the CUT signatures. The proposed techniques guarantee simple design with a very high fault coverage for single stuck-line faults, with low CPU simulation time, and acceptable area overhead. Design algorithms are proposed in the paper, and the simplicity and ease of their implementations are demonstrated with numerous examples. Specifically, extensive simulation runs on ISCAS 85 combinational benchmark circuits with FSIM, ATALANTA, and COMPACTEST programs confirm the usefulness of the suggested approaches.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it