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Record W2120428171

Using Sparse Crossbars within LUT Clusters

2001· article· en· W2120428171 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCrossbar switchLookup tableParallel computingComputer scienceOverhead (engineering)Path (computing)Spare partCluster (spacecraft)Routing (electronic design automation)Field-programmable gate arrayBlock (permutation group theory)Sparse matrixAlgorithmTopology (electrical circuits)Embedded systemMathematicsComputer networkEngineeringCombinatoricsPhysicsTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

In FPGAs, the internal connections in a cluster of lookup tables (LUTs) are often fully-connected like a full crossbar. Such a high degree of connectivity makes routing easier, but has significant area overhead. This paper explores the use of sparse crossbars as a switch matrix inside the clusters between the cluster inputs and the LUT inputs. We have reduced the switch densities inside these matrices by 50% or more and saved from 10 to 18% in area with no degradation to critical-path delay. To compensate for the loss of routability, increased compute time and spare cluster inputs are required. Further investigation may yield modest area and delay reductions.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.910
Threshold uncertainty score0.343

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 head0.087
GPT teacher head0.293
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

Quick stats

Citations49
Published2001
Admission routes1
Has abstractyes

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