Fake Timer: An Engine for Accurate Timing Estimation in Register Transfer Level Designs
Bibliographic record
Abstract
Despite advances in register-transfer level (RTL) synthesis tools, the quality of synthesized netlists still relies on the RTL micro-architecture. This often causes timing violations to be addressed in the RTL, resulting in time-consuming modify-synthesize-analyze cycles. Recent approaches use machine learning (ML) to estimate timing metrics from an RTL. However, they do not target relevant metrics such as block-based arrival times (ATs) and slacks, essential for micro-architecture search. This paper introduces a novel approach called Fake Timer, which back-annotates realistic timing metrics from flattened synthesis to RTL blocks. Fake Timer inputs an RTL intermediate representation and propagates ML-predicted pin-to-pin delays to compute block-based ATs. These computed ATs do not consider cross-boundary optimizations possible during flattened synthesis. Thus, predicted delays are corrected and used to recompute realistic ATs, required ATs, and slacks. Experiments show that computed ATs at the design’s endpoints achieve a coefficient of determination $R^{2}$ of 95% and 85% regarding hierarchical and flattened synthesis results, respectively. Moreover, all the pin-to-pin delays are corrected to consider flattened results and obtain realistic timing metrics. Fake Timer back-annotates timing to RTL blocks, even if they no longer appear in the flattened netlist. In this way, timing-driven micro-architecture search is enabled at the early stages of the design flow.
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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 | 0.001 |
| 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".