The Effect of Compiler Optimizations on High-Level Synthesis-Generated Hardware
Bibliographic record
Abstract
We consider the impact of compiler optimizations on the quality of high-level synthesis (HLS)-generated field-programmable gate array (FPGA) hardware. Using an HLS tool implemented within the state-of-the-art LLVM compiler, we study the effect of compiler optimizations on the hardware metrics of circuit area, execution cycles, FMax , and wall-clock time. We evaluate 56 different compiler optimizations implemented within LLVM and show that some optimizations significantly affect hardware quality. Moreover, we show that hardware quality is also affected by some optimization parameter values, as well as the order in which optimizations are applied. We then present a new HLS-directed approach to compiler optimizations, wherein we execute partial HLS and profiling at intermittent points in the optimization process and use the results to judiciously undo the impact of optimization passes predicted to be damaging to the generated hardware quality. Results show that our approach produces circuits with 16% better speed performance, on average, versus using the standard -O3 optimization level.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".