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Record W2115424089 · doi:10.1109/pacrim.1989.48304

High-speed 2-D hardware convolution architecture based on VLSI systolic arrays

2003· article· en· W2115424089 on OpenAlexaff
D. D. Haule, A.S. Malowany

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsMcGill University
Fundersnot available
KeywordsVery-large-scale integrationConvolution (computer science)Computer scienceSystolic arrayChipCMOSComputer hardwareImage processingParallel computingEmbedded systemImage (mathematics)Artificial intelligenceElectronic engineeringArtificial neural networkEngineering

Abstract

fetched live from OpenAlex

The design of a special-purpose systolic image convolution processor for use in a robot vision system is described. It presents a high-speed two-dimensional hardware convolution architecture based on VSLI systolic arrays for image processing applications. An architecture for the parallel processing of the generalized two-dimensional convolution is summarized. A VLSI convolution chip was designed to accommodate various convolution window sizes. The number of coefficients being handled is directly proportional to the number of systolic computing elements (processors) used. In the present design three such processors are configured on one VLSI chip. Signed coefficients and unsigned data of eight bits are supported. All processing and interprocessor communications are performed bit-serially. The chip design incorporates error detection during convolution, and overflow avoidance techniques are possible for maximum system autonomy. The chip is estimated to operate at a maximum frequency of 16 MHz.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.441
Threshold uncertainty score0.636

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 head0.006
GPT teacher head0.180
Teacher spread0.174 · 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

Citations4
Published2003
Admission routes1
Has abstractyes

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