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Record W2089966668 · doi:10.1145/1950413.1950484

Regular fabric for regular FPGA (abstract only)

2011· article· en· W2089966668 on OpenAlexaff
Xun Chen, Jianwen Zhu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsField-programmable gate arrayTileDesign for manufacturabilityComputer scienceOverhead (engineering)Routing (electronic design automation)Block (permutation group theory)Reduction (mathematics)Process (computing)Embedded systemProcess variationComputer hardwareEngineeringElectrical engineeringMathematicsMaterials science

Abstract

fetched live from OpenAlex

In the sub-wavelength regime, design for manufacturability (DFM) becomes increasingly important for field programmable gate arrays (FPGAs). In this paper, we report an automated tile generation flow targeting micro-regular fabric, this flow automatically generate the basic FPGA tile building block in a standard cell format and then form the whole tile with the help of commercial placing and routing tools. Using a publicly accessible, well-documented academic FPGA as case study, we found that comparing to the tile generators previously reported, our generated micro-regular tile incurs less than 10% area overhead, which could be potentially recovered by process window optimization thanks to its superior printability. In addition, we demonstrate that on 45nm technology, the generated FPGA tile reduces lithography induced process variation by 33%; and reduce probability of failure by 21.2%. If further overhead of 10% area can be recovered by enhanced resolution, we can achieve the variation reduction of 93.8% and reduce probability of failure by 16.2%.

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 score0.903
Threshold uncertainty score0.540

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 head0.032
GPT teacher head0.207
Teacher spread0.176 · 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
Published2011
Admission routes1
Has abstractyes

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