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Record W2128947381 · doi:10.1109/tvlsi.2008.917545

Practical Asynchronous Interconnect Network Design

2008· article· en· W2128947381 on OpenAlexaff
B.R. Quinton, Mark R. Greenstreet, Steven J. E. Wilton

Bibliographic record

VenueIEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2008
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAsynchronous communicationClock skewComputer sciencePipeline (software)InterconnectionAsynchronous systemDesign flowBlock (permutation group theory)Asynchronous circuitIntegrated circuit designComputer architectureEmbedded systemDesign space explorationSkewElectronic engineeringSynchronous circuitEngineeringClock signalComputer networkJitterTelecommunications

Abstract

fetched live from OpenAlex

The implementation of interconnect is becoming a significant challenge in modern integrated circuit (IC) design. Both synchronous and asynchronous strategies have been suggested to manage this problem. Creating a low skew clock tree for synchronous inter-block pipeline stages is a significant challenge. Asynchronous interconnect does not require a global clock, and therefore, it has a potential advantage in terms of design effort. This paper presents an asynchronous interconnect design that can be implemented using a standard application-specific IC flow. This design is considered across a range of IC interconnect scenarios. The results demonstrate that there is a region of the design space where the implementation provides an advantage over a synchronous interconnect by removing the need for clocked inter-block pipeline stages, while maintaining high throughput. Further results demonstrate a computer-aided design tool enhancement that would significantly increase this space. A detailed comparison of power, area, and latency of the two strategies is also provided for a range of IC scenarios.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.237
Teacher spread0.212 · 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.

Study designSimulation or modeling
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

Citations20
Published2008
Admission routes1
Has abstractyes

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