Designing an IEEE-Compliant FPU that Supports Configurable Precision for Soft Processors
Bibliographic record
Abstract
Field Programmable Gate Arrays (FPGAs) are commonly used to accelerate floating-point (FP) applications. Although researchers have extensively studied FPGA FP implementations, existing work has largely focused on standalone operators and frequency-optimized designs. These works are not suitable for FPGA soft processors which are more sensitive to latency, impose a lower frequency ceiling, and require IEEE FP standard compliance. We present an open-source floating-point unit (FPU) for FPGA RISC-V soft processors that is fully IEEE compliant with configurable levels of FP precision. Our design emphasizes runtime performance with 25% lower latency in the most common instructions compared to previous works while maintaining efficient resource utilization. Our FPU also allows users to explore various mantissa widths without having to rewrite or recompile their algorithms. We use this to investigate the scalability of our reduced-precision FPU across numerous microbenchmark functions as well as more complex case studies. Our experiments show that applications like the discrete cosine transformation and the Black-Scholes model can realize a speedup of more than 1.35x in conjunction with a 43% and 35% reduction in lookup table and flip-flop resources while experiencing less than a 0.025% average loss in numerical accuracy with a 16-bit mantissa width.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".