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Record W2517156619 · doi:10.1109/fccm.2016.43

An Empirical Analysis of the Fidelity of VPR Area Models

2016· article· en· W2517156619 on OpenAlexaff
Farheen Khan, Andy Ye

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAdderComputer scienceMultiplexerPoint (geometry)FidelityMultiplexingMathematicsLatency (audio)Telecommunications

Abstract

fetched live from OpenAlex

This work provides an empirical analysis on the fidelity of the VPR area models. Both the original minimum width transistor area model and the new COFFE model are compared against actual layouts with up to 3 metal layers of the various FPGA building blocks. We found that both models have significant variations with respect to the actual layout area. Most importantly both models offer relatively low fidelity in layout area estimation with the widely used original VPR model overestimates layout area of larger buffers and full adders by as much as 22%-34% while underestimates the layout area of smaller buffers and multiplexers by as much as -43%. The newer COFFE model also significantly overestimates the layout area of a full adder by 13% and underestimates the layout area of multiplexers by -55% to -30%. Such a variation is particularly significant considering many previous architectural studies based on these models have differentiated architectures based on the area or area delay product variations as low as a few percentage points. Our results suggest that the actual layout area must be used to achieve a highly accurate FPGA area model.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.614
Threshold uncertainty score0.138

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 head0.037
GPT teacher head0.276
Teacher spread0.239 · 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 designBench or experimental
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

Citations1
Published2016
Admission routes1
Has abstractyes

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