MétaCan
Menu
Back to cohort
Record W1979785091 · doi:10.1109/ccece.2012.6334941

Performance optimization of a data transfer controller for parallel matrix multiplication in FPGAS

2012· article· en· W1979785091 on OpenAlexaff
Ahmad Khayyat, Naraig Manjikian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsStratixComputer sciencePipeline (software)Field-programmable gate arrayController (irrigation)Host (biology)Parallel computingEmbedded systemMemory controllerMultiplication (music)Matrix multiplicationComputer hardwareComputer architectureOperating system

Abstract

fetched live from OpenAlex

This paper describes performance optimizations of a transfer controller for an FPGA-based blocked parallel matrix multiplication accelerator. One of the key challenges of the controller is the generation of a sequence of host memory addresses to transfer blocks of matrices between host and on-chip memories. These addresses are not contiguous, thereby introducing complexity for the controller design. This paper first outlines the intended system architecture for this controller. Next, detailed controller specifications are presented for generating host memory addresses. Various pipeline configurations that yield incremental performance improvements are then described. Finally, experimental results are presented, with the best configuration having an operating frequency exceeding 470 MHz on an Altera Stratix III chip. This level of performance is comparable to that of the pipelined floating-point arithmetic units in the complete system.

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)0.001
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: Methods
Teacher disagreement score0.492
Threshold uncertainty score0.316

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.047
GPT teacher head0.312
Teacher spread0.265 · 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
GenreMethods

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

Citations2
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicParallel Computing and Optimization TechniquesFrench-language works237,207