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Record W2921538540 · doi:10.1109/isscc.2019.8662322

6.2 A 60Gb/s PAM-4 ADC-DSP Transceiver in 7nm CMOS with SNR-Based Adaptive Power Scaling Achieving 6.9pJ/b at 32dB Loss

2019· article· en· W2921538540 on OpenAlexaff
Marc-Andre LaCroix, Henry Wong, Yun Hua Liu, H. Ho, Semyon Lebedev, Petar Krotnev, Dorin Alexandru Nicolescu, Dmitry A. Petrov, C. Nunes de Carvalho, S. Alie, Euhan Chong, F. A. S. Musa, Davide Tonietto

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsHuawei Technologies (Canada)
Fundersnot available
KeywordsSerDesCMOSTransceiverComputer scienceDigital signal processingContext (archaeology)Electronic engineeringEmbedded systemComputer hardwareEngineering

Abstract

fetched live from OpenAlex

With the introduction of PAM-4 signaling at 56Gb/s and the decreased benefits of CMOS scaling for high-speed mixed-signal designs, SerDes designers and system architects are faced with severe performance versus power budget constraints. Power management and energy efficiency have become the main drivers for system design. However, industry standards such as EEE have failed to keep up with efficiency demands. In this context the choice between a so-called analog mixed signal (AMS) SerDes architecture vs. an ADC-DSP-based one has been debated at length. AMS provides significantly lower maximum power [2, 4] while ADC-DSP provides higher link margin [1] thus avoiding expensive and power hungry repeater ICs that largely negate the power advantage of AMS SerDes in a system. AMS provides an easier and cheaper approach to implement multi-tap DFEs [3] compared to DSP where it is typically very expensive to implement more than a 1-tap DFE. This paper will show an ADC-DSP SerDes transceiver with a 2-tap DFE is capable of operating error-free over a 38dB link yet having an overall power budget similar to AMS. The same basic SerDes architecture is implemented (Fig. 6.2.1) with minor differences in 16nm and 7nm FinFET, however, power scaling is incorporated into the 7nm version only.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

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)0.0050.002

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 head0.004
GPT teacher head0.157
Teacher spread0.153 · 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

Citations64
Published2019
Admission routes1
Has abstractyes

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