On error tolerance and Engineering Change with Partially Programmable Circuits
Bibliographic record
Abstract
The growing size, density and complexity of modern VLSI chips are contributing to an increase in hardware faults and design errors in the silicon, decreasing manufacturing yield and increasing the design cycle. The use of Partially Programmable Circuits (PPCs) has been recently proposed for yield enhancement with very small overhead. This new circuit structure is obtained from conventional logic by replacing some subcircuits with programmable LUTs. The present paper lays the theoretical groundwork for evaluating PPCs with Quantified Boolean Formula (QBF) satisfiability. First, QBF models are constructed to calculate the fault tolerance and design error tolerance of a PPC, namely the percentages of faults and design errors that can be masked using LUT reconfigurations. Next, zero-cost Engineering Change Order (ECO) in PPCs is investigated. QBF formulations are given for performing ECOs, and for quantifying the ECO coverage of a PPC architecture. Experimental results are presented evaluating PPCs from [1], demonstrating the applicability and accuracy of the proposed formulations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".