Tagged probabilistic simulation based error probability estimation for better-than-worst case circuit design
Bibliographic record
Abstract
Better than worst-case (BWC) design is an design emerging paradigm in which the conservative frequency guard-bands used in conventional designs are removed at the expense of introducing a a non-zero (but small) error probability. A fundamental challenge in the design of better-than-worst-case circuits is to devise scalable and accurate techniques for error-probability estimation - in this paper we present a new solution to address this challenge using the concept of tagged probabilistic simulations (TPS), which were first introduced in the context of dynamic power estimation. We show that TPS can, in comparison to the existing state-of-the-art, (a) provide consistent speed-up over error probability estimation using timing simulations; and (b) simultaneously provide estimates of both dynamic power dissipation and error probability. To illustrate the benefits of TPS based error probability estimation, we propose two power optimization techniques: a) a gate-level dual-VDD assignment tool b) a gate-sizing technique which optimize the cells used in a design for a certain error and power constraints.
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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 | 0.001 |
| 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".