High Performance and Energy Efficient Floating-Point Multiplier on FPGA
Bibliographic record
Abstract
In this paper, a high performance and energy efficient double-precision floating-point multiplier is designed and implemented on FPGA devices. A novel mapping solution of the mantissa multiplier is proposed which makes full use of the DSP blocks and requires less pipeline stages. In addition, a dual-mode floating-point multiplier is also proposed in this paper which is designed by splitting the components of the proposed double-precision multiplier. Two parallel single-precision operations are supported. For comparison purpose, the proposed architecture is implemented on Xilinx Virtex-5 (xc5vlx155ff1760-3) device, where the proposed double-precision multiplier can run 3.4% faster than previous work with less latency and can run 32.3% faster than the IP core multiplier with same latency. The proposed dual-mode multiplier can run 20.9% faster than previous fastest dual-mode design. In terms of energy consumption, the proposed double-precision multiplier consumes 43.3% less energy per operation compared to the double-precision IP core. The proposed dual-mode multiplier can achieve 24.5% less energy per operation compared to the double-precision IP core. The implementation results of the proposed architectures on latest Xilinx Virtex-7 and Altera Arria-10 devices are provided.
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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 | 0.000 |
| 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".