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Record W1987899988 · doi:10.1109/eeei.2010.5661940

Instruction set extensions for computation on complex floating point numbers

2010· article· en· W1987899988 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
TopicNumerical Methods and Algorithms
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceSubtractionParallel computingSpeedupInstruction setArithmeticComputationMultiplier (economics)Floating pointInstructions per cycleMultiplication (music)Set (abstract data type)Computer hardwareAlgorithmProgramming languageCentral processing unitMathematics

Abstract

fetched live from OpenAlex

We present a pipelined 32-bit Instruction Set Extension (ISE) for complex valued floating point operations. The ISE was implemented in the NIOS II processor, and the constraint on the number of inputs and outputs of the register bank was overcome by distributing the reads and writes of the instruction over several cycles. The hardware size was reduced by sharing hardware between instructions. The main contribution of this work is that the designed ISE performs division, multiplication, addition and subtraction on complex valued numbers. Comparing the use of the embedded multiplier and divider in a NIOS II processor to the designed ISE for an image processing problem, a speedup of 12.2 times was observed.

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: Methods
Teacher disagreement score0.936
Threshold uncertainty score0.285

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 head0.057
GPT teacher head0.351
Teacher spread0.294 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

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