Design re-use for compile time reduction in FPGA high-level synthesis flows
Bibliographic record
Abstract
High-level synthesis (HLS) raises the level of abstraction for hardware design through the use of software methodologies. An impediment to productivity in HLS flows, however, is the run-time of the back-end toolflow - synthesis, packing, placement and routing - which can take hours or days for the largest designs. We propose a new back-end flow for HLS that makes use of pre-synthesized and placed "macros" for portions of the design, thereby reducing the amount of work to be done by the back-end tools, lowering run-time. A key aspect of our work is an analytical placement algorithm capable of handling large macros whose internal blocks have fixed relative placements, in conjunction with placing the surrounding individual logic blocks. In an experimental study, we consider the impact on run-time and quality-of-results of using macros: 1) in synthesis alone, and 2) in synthesis, packing and placement. Results show that the proposed approach reduces run-time by ~3x, on average, with a negative performance impact of ~5%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".