NOVEL DESIGN AND FPGA IMPLEMENTATION OF DA-RNS FIR FILTERS
Bibliographic record
Abstract
Field programmable gate array (FPGA)-based digital signal processing has been widely used in multimedia applications. By combining distributed arithmetic (DA) and residue number system (RNS) in such designs, efficient area, speed and power efficiency can be achieved. In this paper, we propose novel techniques for the design and FPGA implementation of DA-RNS finite impulse response (FIR) filters. By introducing a novel low-cost moduli set and its selection method, efficient modulo arithmetic units inside the subfilters are designed. Then, a new residue-to-binary conversion algorithm, a so-called modified DA Chinese remainder theorem, is derived to reduce the modulo operations and provide an efficient residue-to-binary converter suitable to FPGA implementation. Based on these proposed techniques, a seventh-order DA-RNS FIR filter is designed, implemented and tested by using Xilinx FPGA tools. The implementation results show that the proposed filter design consumes only 77% of the power that the existing filter12,13 requires, while maintaining the same speed (throughput).
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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 | 0.000 |
| Open science | 0.001 | 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".