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Record W1915324130 · doi:10.1109/icecs.2002.1046213

High speed asynchronous structures for inter-clock domain communication

2003· article· en· W1915324130 on OpenAlexaff
Avik Chattopadhyay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsMcGill University
Fundersnot available
KeywordsAsynchronous communicationComputer scienceFIFO (computing and electronics)Asynchronous systemAsynchronous circuitModular designDatapathSynchronizerClock skewBlock (permutation group theory)Clock domain crossingThroughputSynchronous circuitElectronic engineeringComputer hardwareEmbedded systemClock signalDistributed computingWirelessComputer networkJitterEngineeringTelecommunications

Abstract

fetched live from OpenAlex

This paper describes a globally asynchronous, locally dynamic system (GALDS) design paradigm. In a GALDS design, many synchronous blocks are inter-connected using dedicated asynchronous links. Each synchronous block is associated with a local clock generator and features dynamic frequency scaling in order to utilize the least possible power for the required performance to be achieved. Two different asynchronous structures are explored in this paper and they each feature high throughput, modular design and high tolerance to metastability errors that occur when communicating between clock domains. These structures utilize a 4-phase dual track asynchronous control circuit to control either a single direction FIFO with data traveling uniquely in one direction or a bidirectional FIFO that is capable of transmitting data simultaneously in both directions by precisely controlling when data has access to a common, shared datapath. These structures have been created in TSMC's CMOSP18 technology.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.008
GPT teacher head0.211
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2003
Admission routes1
Has abstractyes

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