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Record W2049882446 · doi:10.1145/2560031

A Multiple-FPGA parallel computing architecture for real-time simulation of soft-object deformation

2014· article· en· W2049882446 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

VenueACM Transactions on Embedded Computing Systems · 2014
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsField-programmable gate arrayComputer scienceScalabilityAccelerationParallel computingDiscretizationConjugate gradient methodThroughputComputational scienceEmbedded systemAlgorithm

Abstract

fetched live from OpenAlex

Hardware-based parallel computing is proposed for acceleration of finite-element (FE) analysis of linear elastic deformation models. An implementation of the Preconditioned Conjugate Gradient algorithm on N Field Programmable Gate Array (FPGA) devices solves the large linear system of equations arising from the FE discretization. The system employs a large number of customized fixed-point computing units with a high-throughput memory architecture. An implementation of this scalable architecture on four Altera EP3SE110 FPGA devices yields a peak performance of 604 Giga Operations per second. This enables haptic simulation of a 3-dimensional deformable object of 21000 elements at an update rate of 400Hz.

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 categoriesMeta-epidemiology (narrow)
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 score0.804
Threshold uncertainty score1.000

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 head0.012
GPT teacher head0.235
Teacher spread0.223 · 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