A unified software approach to specify pipeline and spatial parallelism in FPGA hardware
Bibliographic record
Abstract
High-level synthesis (HLS) is increasingly becoming a mainstream design methodology for FPGAs. Whereas its previous applications were mostly limited to research and simple designs, it is now being used to tape-out real-world chips in production [1]. Advances in compiler and HLS research continue to improve the quality of HLS-generated hardware. Despite this, the ease-of-use of HLS tools remains a hurdle to its broad uptake, particularly by engineers without hardware skills. To this end, we propose using a well-known software technique to infer streaming parallel hardware in HLS. Specifically, we use the producer-consumer pattern, commonly used in multi-threaded programming, to infer the generation of hardware that can exploit both pipeline and spatial parallelism on FPGAs. Our proposed methodology allows one to create a design in software, using only standard software methodologies, that cannot only synthesize to streaming hardware, but also model the generated hardware more accurately than existing solutions from other state-of-the-art C-based HLS tools. We use four different real-world benchmarks to illustrate the use of our methodology, and how it can create circuits that are either pipelined, or pipelined and replicated, all from software. For comparison, we also use a commercial HLS tool to synthesize one of the benchmarks, and show that our methodology can produce competitive results to that of the commercial tool.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".