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Record W2046857741 · doi:10.1109/jssc.2005.858618

A VLSI Analog Computer/Digital Computer Accelerator

2005· article· en· W2046857741 on OpenAlexfundno aff
Glenn Cowan, Robert Melville, Y. Tsividis

Bibliographic record

VenueIEEE Journal of Solid-State Circuits · 2005
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAnalog computerComputer scienceIntegratorAnalog signalAnalog signal processingVery-large-scale integrationHybrid computerMicroprocessorWorkstationAnalogue electronicsAnalog deviceComputer hardwareDigital signal processingElectrical engineeringElectronic circuitEmbedded systemEngineering

Abstract

fetched live from OpenAlex

The design of a single-chip VLSI analog computer fabricated in a 0.25-/spl mu/m CMOS process is described. It contains 80 integrators, 336 other linear and nonlinear analog functional blocks, switches for their interconnection, and circuitry to enable the system's programing and control. The IC is controlled, programmed and measured by a PC via a data acquisition card. This arrangement has been used to simulate ordinary differential equations (ODEs), partial differential equations, and stochastic differential equations with moderate accuracy, significantly faster than a modern workstation. Techniques for using the digital computer to refine the solution from the analog computer are presented. Solutions from the analog computer have been used to accelerate a digital computer's solution of the periodic steady state of an ODE by more than 10/spl times/. The IC occupies 1 cm/sup 2/ and consumes 300 mW. An analysis has been done showing that the analog computer dissipates 0.02% to 1% of the energy of a general purpose digital microprocessor and about 2% to 20% of the energy of a digital signal processor, when solving the same differential equation.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0350.009

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 head0.227
Teacher spread0.211 · 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

Citations103
Published2005
Admission routes1
Has abstractyes

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