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Record W1990050178 · doi:10.1109/rsp.2014.6966895

System-on-chip processor using different FPGA architectures in the VTR CAD flow

2014· article· en· W1990050178 on OpenAlexafffund
Jingjing Li, Konstantin Nasartschuk, Kenneth B. Kent

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsUniversity of New Brunswick
FundersCMC Microsystems
KeywordsField-programmable gate arrayVerilogAdderComputer scienceRouting (electronic design automation)Embedded systemBlock (permutation group theory)Design flowComputer architectureComputer hardwareElectronic design automationFPGA prototypeGate array

Abstract

fetched live from OpenAlex

Field Programmable Gate Arrays (FPGA) are often the go to choice for system prototyping and comparison. Circuit design and the impact of hardware architecture can be measured and experimented with using short iteration times. The Verilog To Routing (VTR) CAD flow offers a framework for synthesis and experimentation with customizable FPGA architectures. This paper describes the implemented ability to use the VTR flow for tests and experiments with an ARM processor. This includes different possible FPGA architectures for supporting the ARM processor. A thorough set of experiments is performed which aims to determine the impact of hard block memories, multipliers and adders. The results suggest that 2 bit adder units and 36*36 multipliers offer a good choice of parameters.

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 score0.414
Threshold uncertainty score0.373

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 head0.014
GPT teacher head0.220
Teacher spread0.206 · 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

Citations3
Published2014
Admission routes2
Has abstractyes

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