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Record W2098686588 · doi:10.1109/async.2005.19

Design of High-Performance Power-Aware Asynchronous Pipelined Circuits in MOS Current Mode Logic

2005· article· en· W2098686588 on OpenAlexafffund
Tin Wai Kwan, M. Shams

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceAsynchronous communicationElectronic circuitLogic gateAsynchronous circuitPower (physics)Current-mode logicMode (computer interface)Current (fluid)Logic synthesisElectronic engineeringElectrical engineeringComputer architectureTelecommunicationsEngineeringClock signalAlgorithm

Abstract

fetched live from OpenAlex

This paper introduces the implementation of multi-GHz power-aware asynchronous pipelined circuits in MOS current-mode logic (MCML). The C-element and double-edge-triggered flip-flop are implemented in MCML and used in the so-called micropipeline circuits. An input data detector is proposed to put the inactive combinational logic into sleep mode. The effects of different layout techniques on the performance and power dissipation of an MCML FIFO are also investigated. Based on post-layout simulation results in a standard 0.18 /spl mu/m CMOS technology, an asynchronous MCML four-stage FIFO demonstrates a throughput of 4 GHz while dissipating 3.7 mW. The MCML C-element dissipates up to 4/spl times/ less power compared to its conventional static counterpart at the same throughput of 1.9 GHz. The asynchronous MCML pipelined four-bit carry-look ahead adder with power-saving mechanism reduces the power dissipation by 32% compared to the one without the power-saving mechanism. The power overhead of the input data detector is only 0.23 mW. The input data detector shuts off the stage power in 2 ns and restores the stage in 150 ps after the presence of the new input.

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: Empirical
Teacher disagreement score0.438
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.229
Teacher spread0.213 · 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

Citations10
Published2005
Admission routes2
Has abstractyes

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