Efficient Multiple-Precision Floating-Point Fused Multiply-Add with Mixed-Precision Support
Bibliographic record
Abstract
In this paper, an efficient multiple-precision floating-point fused multiply-add (FMA) unit is proposed. The proposed FMA supports not only single-precision, double-precision, and quadruple-precision operations, as some previous works do, but also half-precision operations. The proposed FMA architecture can execute one quadruple-precision operation, or two parallel double-precision operations, or four parallel single-precision operations, or eight parallel half-precision operations every clock cycle. In addition to the support of normal FMA operations, the proposed FMA also supports mixed-precision FMA operations and mixed-precision dot-product operations. Specifically, the products of two lower precision multiplications can be accumulated to a higher precision addend. By setting the operands of one multiplication to zeros, the proposed FMA can also perform mixed-precision FMA operations. Support for mixed-precision FMA and mixed-precision dot-product is newly added but it only consumes 6.5 percent more area compared to a normal multiple-precision FMA unit. Compared to the state-of-the-art multiple-precision FMA design, the proposed FMA supports more floating-point operations such as half-precision FMA operations and mixed-precision operations with only 10.6 percent larger area.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".