Accurate and Efficient Estimation of Logic Circuits Reliability Bounds
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
As the sizes of CMOS devices rapidly scale deep into the nanometer range, the manufacture of nanocircuits will become extremely complex and will inevitably introduce more defects, including more transient faults that appear during operation. For this reason, accurately calculating the reliability of future designs will be extremely critical for nanocircuit designers as they investigate design alternatives to optimize the tradeoffs between area-power-delay and reliability. However, accurate calculation of the reliability of large and highly connected circuits is complex and very time consuming. This paper presents a complete solution for estimating logic circuit reliability bounds with high accuracy in reasonable time, even for very large and complex circuits. The solution combines a novel criticality scoring algorithm to rank the reliability of individual input vectors with a heuristic search to find the input vector having the lowest reliability. The solution scales well with circuit size, and is independent of the interconnect complexity or the logic depth. Extensive computational results show that the speed of our method is orders of magnitude faster than exact solutions provided by Bayesian network exact inferences, while maintaining identical or sufficiently close accuracy.
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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