Area‐ and power‐efficient iterative single/double‐precision merged floating‐point multiplier on FPGA
Bibliographic record
Abstract
In this study, an area and power‐efficient iterative floating‐point (FP) multiplier architecture is designed and implemented on FPGA devices with pipelined architecture. The proposed multiplier supports both single‐precision (SP) and double‐precision (DP) operations. The operation mode can be switched during run time by changing the precision selection signal. The Karatsuba algorithm is applied when mapping the mantissa multiplier in order to reduce the number of digital signal processing (DSP) blocks required. For DP operations, the iterative method is applied which require much less hardware than a fully pipelined DP multiplier and thus reduces the power consumption. To further reduce the power consumption, the unused logic blocks for a specific operation mode are disabled. Compared to previous work, the proposed multiplier can achieve 33% reduction of DSP blocks, 4.3% less look‐up tables (LUTs), and 31.2% less flip‐flops while having 4% faster clock frequency on Virtex‐5 devices. Compared to the intellectual property core DP multiplier provided by the FPGA vendors, the proposed multiplier required less DSP blocks and achieves lower‐power consumption. The mapping solutions and implementation results of the proposed multiplier on Xilinx Virtex‐7 and Altera Arria‐10 devices are also presented. In addition, the results of a direct implementation of the proposed architecture on STM‐90 nm ASIC platform are reported.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".