Efficient Scheme for Implementing Large Size Signed Multipliers Using Multigranular Embedded DSP Blocks in FPGAs
Bibliographic record
Abstract
Modern FPGAs contain embedded DSP blocks, which can be configured as multipliers with more than one possible size. FPGA‐based designs using these multigranular embedded blocks become more challenging when high speed and reduced area utilization are required. This paper proposes an efficient design methodology for implementing large size signed multipliers using multigranular small embedded blocks. The proposed approach has been implemented and tested targeting Altera′s Stratix II FPGAs with the aid of the Quartus II software tool. The implementations of the multipliers have been carried out for operands with sizes ranging from 40 to 256 bits. Experimental results demonstrated that our design approach has outperformed the standard scheme used by Quartus II tool in terms of speed and area. On average, the delay reduction is about 20.7% and the area saving, in terms of ALUTs, is about 67.6%.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".