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Record W2789985954 · doi:10.1145/3130265.3130326

Simulation-based circuit-activity estimation for FPGAs containing hard blocks

2017· article· en· W2789985954 on OpenAlexafffund
Sean Seeley, Vidya Sankaranaryanan, Zack Deveau, Panagiotis Patros, Kenneth B. Kent

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsComputer scienceField-programmable gate arrayBenchmark (surveying)VerilogElectronic circuitRouting (electronic design automation)Embedded systemElectronic design automationSoftwareComputer hardwareElectronic engineeringComputer architectureEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

FPGAs are electronic devices that are programmable and can functionally perform equivalently to a number of other circuits. FPGAs are used for both rapid and cheap prototyping of new circuit designs as well as for replacing outdated chip models. Due to their complexity, circuits cannot be practically designed by hand; instead, specialized Computer Aided Design (CAD) software performs this complex task. A major concern for devices is power requirements, which can have adverse effects on both the environment and users. The power requirements of a circuit can be directly connected with its activity, which can be estimated by the CAD tools. In this work, we focus on the open source Verilog-To-Routing (VTR) CAD software and propose an improved activity estimation tool using VTR's synthesizer (Odin II) that extends beyond the capabilities of its current estimator (ACE2), such as proper black box activity propagation and support for circuits containing no clocks or more than one clock. Our results are experimentally evaluated with VTR's FPGA architectures and benchmark circuits.

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: none
Teacher disagreement score0.947
Threshold uncertainty score0.415

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 head0.054
GPT teacher head0.298
Teacher spread0.244 · 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

Citations2
Published2017
Admission routes2
Has abstractyes

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