FPGA design of an efficient divide-and-conquer multiplier based on multiple stage fast ripple hybrid adders for peak cancellation with IIR filters
Bibliographic record
Abstract
The evolution of Digital Signal Processing (DSP) systems within Very Large Scale Integration (VLSI) era has significantly impacted computational speed, chip size, and power consumption, consequently influencing the overall cost of systems. Typically, sophisticated DSP systems, including peak cancellation with infinite impulse response (PC-IIR) filters, require multiple arithmetic units. Therefore, the performance of PC-IIR filters can be significantly improved by efficient design of arithmetic logic circuits. In this paper, a new multiple-stage fast ripple hybrid adder (MS-FRHA) and Compressor-based divide-and-conquer vector multiplier (CDCVM) is introduced for PC-IIR filter. Multiple single-stage carry select structures are combined in the proposed MS-FRHA to increase area, delay, and power performance. Also, CDCVM effectively handles huge numbers by dividing the multiplication problem into smaller sub-problems and uses compressor-based Vedic multiplication (CVM) for each sub-problem. According to simulation data, the proposed arithmetic logic circuits use the least amount of space, time, and power of all the earlier designs. Furthermore, a comparison to the most advanced PC-IIR filter shows that the proposed model can reduce delays and resource consumption.
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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 | 0.000 |
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".