Efficient Performance Modeling for Automated CMOS Analog Circuit Synthesis
Why this work is in the frame
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Bibliographic record
Abstract
Fast and accurate performance estimation can significantly enhance the efficiency of automated analog circuit synthesis. This article presents a novel performance modeling method that can efficiently estimate circuit performance with ignorable model building overhead for variant circuit topologies. The proposed method starts with accurate transistor modeling by taking advantage of the advanced neural network (NN) fitting technique. It then utilizes the established transistor models and topology information from a circuit netlist to precisely discover the circuit dc operating point. Specialized deterministic schemes have been developed with the aid of an undirected bipartite graph converted from the circuit netlist. Moreover, the accurate NN transistor models help directly derive the small-signal model parameter values, which can be further applied to conduct symbolic analysis to evaluate circuit performances. Our experimental results not only compare various deterministic dc operating point computation schemes but also demonstrate the efficient model development, general applicability, speedy execution, and fair prediction of our proposed performance modeling method.
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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