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Record W2120960466 · doi:10.1109/isspit.2006.270806

Area-Efficient FIR Filter Design on FPGAs using Distributed Arithmetic

2006· article· en· W2120960466 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDigital Filter Design and Implementation
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsStratixLookup tableField-programmable gate arrayFinite impulse responseComputer scienceAccumulator (cryptography)Multiplier (economics)Parallel computingAdderFilter (signal processing)Computer hardwareArithmeticEmbedded systemAlgorithmMathematicsLatency (audio)

Abstract

fetched live from OpenAlex

In this paper, a highly area-efficient multiplier-less FIR filter is presented. Distributed Arithmetic (DA) has been used to implement a bit-serial scheme of a general asymmetric version of an FIR filter, taking optimal advantage of the 4-input LUT-based structure of FPGAs. Furthermore, we have introduced a modification in the accumulator stage to achieve further savings. The proposed filter has been designed and synthesized with Altera Quartus II, and implemented on a Stratix FPGA device. Our results show reduced area requirements in comparison to previous LUT-less DA architectures

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 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)0.000
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.889
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

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 science0.0000.000
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 head0.073
GPT teacher head0.268
Teacher spread0.195 · 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

Quick stats

Citations61
Published2006
Admission routes1
Has abstractyes

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