Efficient analog platform characterization through analog constraint graphs
Bibliographic record
Abstract
We propose a scheme for improving the efficiency of the characterization process for system-level models of analog circuits within the analog platform based design paradigm. We leverage designer knowledge to map basic functional requirements of the circuit into circuit parameters relations so that the sampling space can be significantly reduced. A set of equalities and inequalities in the circuit parameters is used to represent the constraints. A feasible parameter space lies at the intersection of the sets of design parameters that satisfy equalities and inequalities, defining a manifold in the parameter space. We introduce a bipartite graph representation denoted analog constraint graphs (ACG) to represent these constraints. ACGs are instrumental for obtaining a random configuration generator that samples configurations in the manifold. The sampler is automatically translated into executable code to fit the characterization framework starting from a mathematical description of constraints. Results show that the automatically generated samplers are comparable in terms of code efficiency with hand-written ones. Furthermore, a heuristics to generate uniformly distributed configuration enabled by the tools is presented and applied to a complex ADC design, yielding a reduction in power consumption by more than 28%.
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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 | 0.000 | 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 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".