Invited Paper: RapidWright: Unleashing the Full Power of FPGA Technology with Domain-Specific Tooling
Bibliographic record
Abstract
The era of domain-specific computing has created an environment that drives for the best performance possible from silicon and tools. Novel and unique implementation strategies for FPGAs that may have been infeasible in the past are now sought after in the search of better performance or faster compile time. The ability to optimize for domain-specific attributes in FPGA implementations is now more important than ever and both industry and research institutions need better ways to fully harness the programmable potential of FPGAs. This paper describes RapidWright, an open source framework from AMD Research and Advanced Development, and how it enables design implementation to be customized on commercial FPGA devices. RapidWright enables strategies previously infeasible or impossible to designers and provides sufficient flexibility to leverage domain-specific attributes in their applications for the highest performance, compile-time, or timing closure predictability. We demonstrate how the RapidWright framework has been a fundamental enabling factor in a variety of practical research efforts that have led up to 30% higher quality-of-result (QoR) and compile time improvements of 5× or greater across a number of applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.014 |
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".