High performance and energy efficient single‐precision and double‐precision merged floating‐point adder on FPGA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A high performance and energy efficient single‐precision and double‐precision merged floating‐point adder based on the two‐path FP addition algorithm designed and implemented on field programmable gate array (FPGA) is presented. With a fully pipelined architecture, the proposed adder can accomplish one double‐precision addition or two parallel single‐precision additions in six clock cycles. The proposed architecture is designed based on the double‐precision adder and each major component is segmented to support dual single‐precision operations. In addition, all the components of the proposed adder are optimised for mapping on FPGA. The proposed architecture is implemented on both Altera Stratix‐III and Xilinx Virtex‐5 devices and it has a faster clock frequency when compared with the double‐precision intellectual property (IP) core adder provided by the FPGA vendors. Since the dual single‐precision operations support, the proposed adder has higher throughput compared with the single‐precision IP core adder. In addition, the proposed adder has better energy efficiency compared with both single‐precision and double‐precision IP core adder. The implementation results of the proposed adder on the latest Altera Arria‐10 and Xilinx Virtex‐7 devices are provided. A direct implementation of the proposed architecture on STM‐90 nm technology ASIC platform is also performed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it