Circuit performance verification and optimization in the presence of variability
Bibliographic record
Abstract
The continued scaling of digital integrated circuits has led to an increasingly larger impact of process, supply voltage, and temperature (PVT) variations. The effect of these variations on logic cell and interconnect delays has introduced challenges to both circuit performance (timing) verification and optimization. In order for us to fully take advantage of the benefits of technology scaling, it is essential that “variation-aware” techniques for performance verification and optimization be developed and used in modern design flows. In this thesis such techniques for both performance verification and optimization are presented. First, we present a fast method for finding the worst-case slacks over all process and environmental corners. This method uses the standard set of PVT corners available in industry, and provides large runtime gains while maintaining a high degree of accuracy. After that, we propose an efficient block-based parameterized timing analysis technique that can accurately capture circuit delays at every point in the parameter space, by reporting all paths that can become critical. This method employs parameterized static timing analysis (PSTA) variability models, and allows one to easily examine local robustness to parameters in different regions of the parameter space. Next, we introduce an optimization method that alters clock network lines so that a circuit meets its timing constraints at all PVT settings under PSTA variability models. This is formulated as a Linear Program (LP), which is based on a clock skew optimization formulation, and as a result it can be solved efficiently. Finally, we present a method that uses characterized, pre-silicon, PSTA variational timing models to identify speedpaths that can best explain the observed delay measurements during silicon debug. This is a crucial step, required for both “fixing”' failing paths and for accurate learning from silicon data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".