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Record W2053237783 · doi:10.1109/mwscas.2007.4488818

A new parallel link interface with current-mode incremental signaling and per-pin skew compensation

2007· article· en· W2053237783 on OpenAlexaff
An Hu, Fei Yuan

Bibliographic record

VenueConference proceedings · 2007
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSkewClock skewComputer scienceClock domain crossingCMOSElectronic engineeringClock rateInterface (matter)Topology (electrical circuits)Synchronous circuitElectrical engineeringClock signalEngineeringTelecommunicationsParallel computingJitter

Abstract

fetched live from OpenAlex

This paper proposes a new current-mode incremental signaling parallel link interface with per-pin skew compensation. Per-pin skew compensation is carried out in a training phase where clock-like training data are sent to all channels along with a reference clock of the same frequency. Training data are deskewed with respect to the common reference clock using DLLs such that all channels are skew-compensated simultaneously. New encoding and decoding scheme have been proposed to reduce the signal critical path at the transmitter. Transimpedance amplifiers with replica biasing are used to perform current-to-voltage conversion at the receiving end with a minimum sensitivity to supply voltage fluctuation. To evaluate the performance of the proposed skew compensating technique, a parallel link interface consisting of two data channels and one reference clock channel has been implemented with UMC 0.13 mum 1.2 V CMOS technology and analyzed using SpectreRF from cadence design systems with BIM3V3 device models. The channels are modeled as 50 Omega microstrip lines. Simulation results have demonstrated that the proposed parallel link interface is capable of deskewing the channel signals at 1 Gbytes/s.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.745
Threshold uncertainty score0.843

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 head0.023
GPT teacher head0.252
Teacher spread0.229 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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