FPGA design of an efficient divide-and-conquer multiplier based on multiple stage fast ripple hybrid adders for peak cancellation with IIR filters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it