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Record W3195707210 · doi:10.1109/asap52443.2021.00043

Double-Pumping the Interconnect for Area Reduction in Coarse-Grained Reconfigurable Arrays

2021· article· en· W3195707210 on OpenAlex
Xinyuan Wang, Tianyi Yu, Hsuan Hsiao, Jason H. Anderson

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
TopicEmbedded Systems Design Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInterconnectionMultiplexerComputer scienceReduction (mathematics)Application-specific integrated circuitWord (group theory)Clock rateComputer hardwareEmbedded systemElectronic engineeringMultiplexingEngineeringTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

We consider double-pumped interconnect as a means of area reduction in coarse-grained reconfigurable arrays (CGRAs). Interconnect multiplexers comprise a considerable portion of CGRA area. We apply double-pumping to halve the word-width of the interconnect multiplexers, saving area. The interconnect is operated at twice the system clock frequency, where the top and bottom half-words of a value are communicated in the first and second half of a clock cycle. Several circuit-level approaches for double-pumping are considered, and evaluated in different CGRA architectures with varied interconnect richness. Area and performance consequences are assessed through a 45nm standard-cell ASIC implementation. Overall CGRA area improvements of up to 16% are observed, depending on the CGRA architecture and double-pumping implementation.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.060
GPT teacher head0.286
Teacher spread0.226 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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