Bibliographic record
Abstract
As VLSI fabrication technology progresses to 65nm feature sizes and smaller, transistors no longer operate as ideal switches. This motivates verifying digital circuits using continuous models. This paper presents the verification of the high-speed, toggle flip-flop proposed by Yuan and Svensson [1]. Our approach builds on the projection based methods originally proposed by Greenstreet and Mitchell [2], [3]. While they were only able to demonstrate their approach with two- and threedimensional systems, we apply projection based analysis to a seven-dimensional model for the flip-flop. We believe that this is the largest verification to date of a digital circuit using non-linear circuit-level models. In this paper, we describe how we overcame problems of numerical errors and instability associated with the original projection based methods. In particular, we present a novel linear-program solver and new methods for constructing accurate linear approximations of non-linear dynamics. We use the toggle flip-flop as an example and consider how these methods could be extended to verify a standard cell library for digital design.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".