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Record W2062468832 · doi:10.1109/isvlsi.2014.27

Design of a Flexible, Energy Efficient (Auto)Correlator Block for Timing Synchronization

2014· article· en· W2062468832 on OpenAlexaff
Fabio Campi, Roberto Airoldi, Jari Nurmi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEmbedded Systems Design Techniques
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputer scienceSynchronization (alternating current)ScalabilityEmbedded systemEnergy consumptionBlock (permutation group theory)Overhead (engineering)Flexibility (engineering)Context (archaeology)Efficient energy useWirelessReliability (semiconductor)Distributed computingPower (physics)Computer networkChannel (broadcasting)EngineeringTelecommunications

Abstract

fetched live from OpenAlex

Multi-mode and multi-standard connectivity has become a necessity for portable communication systems. A convenient architectural solution is to build flexible systems that can be reprogrammed to meet requirements of multiple standards. One of the major issues in this context is the resource overhead required by programmability. In particular, in the latest VLSI technology nodes, energy consumption has become a very severe problem, greatly impacting the reliability of the hardware. Therefore, any design aimed at the implementation of multi-mode multi-standard communication systems must be strictly targeted at the lowest power consumption without jeopardizing peak performance, while, at the same time, retaining a high degree of flexibility. This work presents the design and implementation of a (auto)correlator block for timing synchronization. The design is composed of a scalable computational unit, which allows to meet real-time requirements of different wireless communication standards (e.g. W-CDMA, IEEE 802.11a/g/n). Moreover, dynamic power management allows to dynamically trade-off energy consumption versus performance, adapting power dissipation to the specific requirements of each supported standard, as well as to follow dynamic variations of the computation load.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.830
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.248
Teacher spread0.225 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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".

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Citations1
Published2014
Admission routes1
Has abstractyes

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