How to achieve worst-case performance [self-timed circuit design]
Bibliographic record
Abstract
"Average case performance" is an oft-cited motivation for self-timed design. In self-timed designs, computations proceed according to handshakes, and these handshakes can reflect the actual time required for operations rather than the worst-case time. The intuitive argument is that this should lead to systems whose performance reflects the average-cease performance of their components. This paper shows that such intuition is often wrong. This paper describes a connection between self-timed circuits and percolation networks. Percolation networks are a class of infinite graphs originally used to model critical phenomena arising from fluid flows in porous media. This paper shows how these techniques can be used to show the frequent existence of long chains of slow operations in self-timed designs. These chains can give rise to performance that is closer to worst-case than average-case. This paper makes three contributions. First, it describes a fundamental connection between percolation networks and self-timed circuits. Second, it presents novel methods for studying percolation networks that arise in the analysis of self-timed circuits. Third, it gives examples of self-timed circuits whose performance is limited by percolation phenomena.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".