Tatum: Parallel Timing Analysis for Faster Design Cycles and Improved Optimization
Why this work is in the frame
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Bibliographic record
Abstract
Static Timing Analysis (STA) is used to evaluate the correctness and performance of a digital circuit implementation. In addition to final sign-off checks, STA is called numerous times during placement and routing to guide optimization. As a result, STA consumes a significant fraction of the time required for design implementation; to make progress reducing FPGA compile times we need faster STA. We evaluate the suitability of both GPU and multi-core CPU platforms for accelerating STA. On core STA algorithms our GPU kernel achieves a 6.2 times kernel speed-up but data transfer overhead reduces this to 0.9 times. Our best CPU implementation achieves a 9.2 times parallel speed-up on 32 cores, yielding a 15.2 times overall speed-up compared to the VPR analyzer, and a 6.9 times larger parallel speed-up than a recent parallel ASIC timing analyzer. We then show how reducing the run-time cost of STA can be leveraged to improve optimization quality, reducing critical path delay by 4%.
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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 it