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Record W4220921822 · doi:10.18280/i2m.210104

Efficient Cached 64 Point FFT Processor Using Floating Point Arithmetic for OFDM Application

2022· article· en· W4220921822 on OpenAlexvenueno aff
C. Padma, Palapati Jagadamba, P. Ramana Reddy

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2022
Typearticle
Languageen
FieldComputer Science
TopicDigital Filter Design and Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsFast Fourier transformComputer scienceOrthogonal frequency-division multiplexingCacheParallel computingArithmeticComputer hardwareMathematicsAlgorithmTelecommunicationsChannel (broadcasting)

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.785
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.317
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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