Efficient Cached 64 Point FFT Processor Using Floating Point Arithmetic for OFDM Application
Bibliographic record
Abstract
Presently Fourth generation and other wireless systems are focused area for the research and development in the communication field. Fast Fourier Transform (FFT) & Inverse FFT (IFFT) are required for Orthogonal frequency division multiplexing in the integral part of modulation/demodulation modules that occupies more area and power. This paper presents low power and area efficient Cached memory for Fast Fourier Transform (FFT) processor using floating point arithmetic for OFDM application. To store computational permutations each butterfly unit needs one memory. So if considering higher radix of FFT processor, memory requirement increases, it yields to more power consumption and more density occupancy. In this proposed cached 64 point radix 2^6 SDF architecture for reducing the arithmetic hardware complexity of complex multipliers and complex adders present in butterfly structure to obtain low power and less memory requirement. The proposed system is synthesized by using CADENCE RTL COMPLIER and is implemented in ENCOUNTER RTL TO GDSII SYSTEM” using 90nm CMOS technology with a supply voltage of 1V. Synthesis results shows that the proposed design is efficient in terms of gate count, area and power 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".