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Record W4238119721 · doi:10.32920/ryerson.14648718.v1

Minimizing the layout area of 2-input look up tables

2021· preprint· en· W4238119721 on OpenAlexaff
Nafiul Hyder

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMultiplexerRouting (electronic design automation)Block (permutation group theory)Computer scienceRouterField-programmable gate arrayIntegrated circuit layoutBenchmark (surveying)Place and routeComputer hardwareMultiplexingEmbedded systemComputer networkIntegrated circuitTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

This work investigates the minimum layout area of multiplexers, a fundamental building block of Field-Programmable Gate Arrays (FPGAs). In particular, we investigate the minimum layout area of 4:1 multiplexers, which are the building blocks of 2-input Look-Up Tables (LUTs) and can be recursively used to build higher order LUTs and multiplexer-based routing switches. We observe that previous work routes all four data inputs of 4:1 multiplexers on a single metal layer resulting in a wiring-area-dominated layout. In this work, we explore the various transistor-level placement options for implementing the 4:1 multiplexers while routing multiplexer data inputs through multiple metal layers in order to reduce wiring area. Feasible placement options with their corresponding data input distributions are then routed using an automated maze router and the routing results are then further manually refined. Through this systematic approach, we identified three 4:1 multiplexer layouts that are smaller than the previously proposed layouts by 30% to 35%. In particular, two larger layouts of the three are only 33% to 45% larger than layout area predicted by the two widely used active area models from previous FPGA architectural studies, and the smallest of the three layouts is 1% to 11% larger than the layout area predicted by these models.

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 score0.873
Threshold uncertainty score0.734

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 head0.025
GPT teacher head0.226
Teacher spread0.201 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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