Syncopation: Adaptive Clock Management for High-Level Synthesis Generated Circuits on FPGAs
Bibliographic record
Abstract
High-level synthesis (HLS) tools improve hardware designer productivity by enabling software design techniques during hardware development. During HLS the delay of paths can only be estimated, so the resulting circuit may suffer from unbalanced computational path delays across clock cycles. Since the maximum operating frequency of circuits is determined statically using the worst-case timing path, unbalanced paths may lead to reduced performance compared to circuits designed at the hardware level. In this paper, we address this using Syncopation, a performance-boosting fine-grained timing analysis and adaptive clock management technique for HLS circuits. The key idea is to use the HLS scheduling information along with the results from placement and routing to determine the worst-case timing path for individual clock cycles. By then adjusting the clock period on a cycle-to-cycle basis, we can increase circuit performance. Our experiments show that Syncopation and fine-grained timing analysis can improve performance without altering the HLS-synthesis toolchain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".