Effective implementation of floating-point adder using pipelined LOP in FPGAs
Bibliographic record
Abstract
The current intellectual property provided by Xilinx for floating-point adder is not competitive and versatile. This paper presents a hardware implementation of IEEE 754 compliant floating-point adder and a design methodology for floating-point adder with leading-one predictor (LOP). LOP has been used to predict the shift amount for post normalization in parallel with the addition. In some cases, however, there is an error in prediction. LOP used in our design detects this error concurrently with the prediction. Xilinx 6.3 ISE was used to synthesize VHDL implementations for five levels of pipeline stage floating-point adder. LOP was pipelined to three stages, to obtain better latency for some Xilinx FPGA devices compared to the current intellectual property. For Spartan 3 and Virtex 2p FPGA architectures with five stage pipeline implementation, 25% improvement in clock speed was achieved using pipelined LOP.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".