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FPGA Implementation for the Multiplexed and Pipelined Building Blocks of Higher Radix-2<sup>k</sup> FFT

2020· article· en· W3016874168 on OpenAlexaff
Daniel Massicotte, Marwan A. Jaber, Chokri Neili, Messaoud Ahmed Ouameur

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDigital Filter Design and Implementation
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsFast Fourier transformComputer scienceThroughputField-programmable gate arrayMultiplexingLatency (audio)Signal processingParallel computingOrthogonal frequency-division multiplexingComputer hardwareComputationDigital signal processingWirelessAlgorithmTelecommunications

Abstract

fetched live from OpenAlex

Fast Fourier transform (FFT) is one of the fundamental processing block used in many signal processing applications (i.e. for orthogonal frequency division multiplexing in wireless telecommunication). Therefore, every proposal to reduced latency, resources or accuracy errors of FFT implementation counts. This paper proposes the implementation of the butterfly processing elements (BPE) where the concept of the radix-r butterfly computation has been formulated as the combination of α radix-2 butterflies implemented in parallel. An efficient FFT implementation is feasible using our proposed multiplexed and pipelined BPE. Compared to a state-of-the-art reference based on pipelined and parallel structure FFTs, and FPGA based implementation reveals that the maximum throughput is improved by a factor of 1.3 for a 256-point FFT and reach a throughput of 2680 MSps on Virtex-7. The analysis extends to touch on key performance measurements metrics such as throughput, latency and resource utilization.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.049
GPT teacher head0.313
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations7
Published2020
Admission routes1
Has abstractyes

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