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Record W2112962774 · doi:10.1109/tvlsi.2008.2001138

A Robust 4-PAM Signaling Scheme for Inter-Chip Links Using Coding in Space

2008· article· en· W2112962774 on OpenAlexaff
K. Farzan, D.A. Johns

Bibliographic record

VenueIEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvancements in PLL and VCO Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChipBit error rateCoding (social sciences)Computer scienceElectronic engineeringCoding gainForward error correctionChannel (broadcasting)CMOSAlgorithmElectrical engineeringTelecommunicationsDecoding methodsEngineeringMathematics

Abstract

fetched live from OpenAlex

Increasing demand for high-speed inter-chip interconnects requires faster links that consume less power. Channel coding can be used to lower the required signal-to-noise ratio for a specific bit error rate in a channel. There are numerous codes that can be used to approach the theoretical Shannon limit, which is the maximum information transfer rate of a communication channel for a particular noise level. However, the complexity of these codes prohibits their use in high-speed inter-chip applications. A low-complexity signaling scheme is proposed here. This method can achieve 3-5-dB coding gain over uncoded four-level pulse amplitude modulation (PAM). The receiver for this signaling scheme along with a regular 4-PAM receiver was designed and implemented in a 0.18-mum standard CMOS technology. Experimental results show that the receiver is functional up to 2.5 Gb/s. This was verified with a bit error rate tester (BERT) and we were able to achieve error free operation at 2.5-Gb/s channel transfer rate. The entire receiver for this scheme consumes 22 mW at 2.5 Gb/s and occupies an area of 0.2 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .

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 categoriesMeta-epidemiology (narrow)
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 score0.772
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.051
GPT teacher head0.258
Teacher spread0.208 · 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

Citations3
Published2008
Admission routes1
Has abstractyes

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