Asymmetric Large Size Signed Multipliers Using Embedded Blocks in FPGAs
Bibliographic record
Abstract
In this paper, asymmetric non-pipelined large size signed multipliers are implemented using symmetric and asymmetric embedded multipliers in FPGAs. Decomposition of the operands, and consequently the multiplication process, are performed for the efficient use of the embedded blocks. Partial products are organized in various configurations, and the additions of the products are performed in an optimized manner. A heuristic method has been developed, which analyzes the timing and the area at each stage of the adder tree. The optimization algorithm, which is referred to as "Delay-Table" method has led to the minimization of the total critical path delay with reduced utilization of FPGA resources. The asymmetric signed multipliers are implemented in Xilinx FPGAs using 18×18-bit and 25×18-bit embedded signed multipliers. The implementation results have demonstrated an improvement in terms of speed and number of embedded blocks compared to the standard realization. The improvements are 27.1% in speed and 10.9% in the use of embedded multipliers when using the symmetric embedded blocks. The improvements increase further to 28.5% and 36.6%, respectively, when using asymmetric embedded multipliers.
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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 | 0.000 | 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".