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Record W1827246489 · doi:10.1109/iscas.2001.921982

An automatic word length determination method

2002· article· en· W1827246489 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 institutionsDepartment of National DefenceNortel (Canada)Polytechnique Montréal
Fundersnot available
KeywordsWord (group theory)Computer scienceDigital signal processingSignal-flow graphAlgorithmDiscrete cosine transformFilter (signal processing)Floating pointWord lengthFixed-point arithmeticRange (aeronautics)Point (geometry)InverseFixed pointTrigonometric functionsMathematicsComputer hardwareArtificial intelligenceComputer vision

Abstract

fetched live from OpenAlex

A method to determine the word length required by implementations of Digital Signal Processing (DSP) algorithms is presented. The method uses a C/C++ fixed-point simulation tool to model the impact of finite word length on overall accuracy. It finds a combination of quasi-optimum bit resolutions in arbitrary data flow graphs by computing dissimilarities between fixed-point and floating-point simulation results. The selected algorithm minimizes these dissimilarities and finds a combination of word lengths that meets objectives specified by the user. This method is applicable to a wide range of DSP algorithms. It was tested on 2 benchmarks, the fifth order elliptic filter and the Inverse Discrete Cosine Transform (IDCT), and arrived to known optimum solutions.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.332

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 communication0.0000.002
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 head0.054
GPT teacher head0.330
Teacher spread0.276 · 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

Citations53
Published2002
Admission routes1
Has abstractyes

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