Adaptive Clock Management of HLS-generated Circuits on FPGAs
Bibliographic record
Abstract
In this article, we present Syncopation , a performance-boosting fine-grained timing analysis and adaptive clock management technique for High-Level Synthesis-generated circuits implemented on Field-Programmable Gate Arrays. The key idea is to use the HLS scheduling information along with the placement and routing results to determine the worst-case timing path for individual clock cycles. By adjusting the clock period on a cycle-by-cycle basis, we can increase performance of an HLS-generated circuit. Our experiments show that Syncopation improves performance by 3.2% (geomean) across all benchmarks (up to 47%). In addition, by employing targeted synthesis techniques along with Syncopation, we can achieve 10.3% performance improvement (geomean) across all benchmarks (up to 50%). Syncopation instrumentation is implemented entirely in soft logic without requiring alterations to the HLS-synthesis toolchain or changes to the FPGA, and has been validated on real hardware.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".