A Decimal Floating-Point Adder with Decoded Operands and a Decimal Leading-Zero Anticipator
Bibliographic record
Abstract
The IEEE 754-2008 Standard for Floating-Point Arithmetic was officially approved this year. One of the most important revisions to IEEE 754-1985 is the introduction of decimal floating-point (DFP) formats and operations. Since IEEE 754-1985 was revised, major microprocessor vendors have been working on hardware designs and software libraries for decimal arithmetic. Because the new standard has been approved, many software vendors are planning to adapt the new decimal formats into their applications. Therefore, it is important to investigate efficient algorithms and hardware designs for common DFP arithmetic operations to improve the performance of these applications. This paper presents a novel DFP adder with decoded operands and a decimal leading-zero anticipator (LZA). The DFP adder is based on a previous DFP adder design with several new features, including a new internal format, an improved operand pre-correction stage, and a novel decimal LZA to obtain better timing for decimal addition and subtraction. Synthesis results show that the new DFP adder is roughly 14% faster than the previous design.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".