On error tolerance and Engineering Change with Partially Programmable Circuits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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 | 0.000 |
| 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