Efficient FPGA implementation of complex multipliers using the logarithmic number system
Bibliographic record
Abstract
In many real-time DSP applications, high performance is a prime target. However, achieving this may be done at the expense of area, power dissipation and accuracy. Attempts have been made to use alternative number systems to optimize the realization of arithmetic blocks, maintaining high performance without incurring prohibitive area and power increases. This paper presents the FPGA implementation of complex multipliers based on the logarithmic number system. Synthesis results show that a design with a 10-stage pipeline can achieve a maximum clock rate of 224 MHz and 140 MHz for 16-bit and 32-bit designs, respectively. Both designs use the lowest amount of hardware in terms of gate equivalents as compared to a complex multiplier built with regular FPGA features. In particular, the proposed architecture uses 67% and 35% fewer gates to implement a 32-bit and 16-bit complex multiplier, respectively, when compared to a design realized with embedded multipliers. Simulation results based on selected test vectors show that the greatest relative error of the logarithmic-based 16-bit complex multiplier is 2.14%
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".