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Record W1942862422 · doi:10.1109/newcas.2004.1359051

An embedded DRAM for MDLNS FIR filter

2004· article· en· W1942862422 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe 2nd Annual IEEE Northeast Workshop on Circuits and Systems, 2004. NEWCAS 2004. · 2004
Typearticle
Languageen
FieldComputer Science
TopicDigital Filter Design and Implementation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDramCAS latencyUniversal memoryComputer scienceCapacitanceCapacitorElectronic engineeringElectrical engineeringFilter (signal processing)Embedded systemStatic random-access memoryDynamic random-access memoryVoltageComputer hardwareEngineeringMemory controllerSemiconductor memoryComputer memoryPhysicsMemory refresh

Abstract

fetched live from OpenAlex

This paper presents an embedded DRAM architecture that was developed specifically for programmable MDLNS FIR filter. A picture of the DRAM module as a complex entity is provided along with detailed operation analysis. The DRAM storage capacitor is formed with diffusion junction capacitance instead of trenched capacitor cell used by the conventional embedded DRAM, allowing seamless integration into logic part of entire design. Half voltage bitline precharge is accomplished by charge-sharing between true bitline and its complement bitline, resulting in minimum power consumption and low hardware complexity with no additional power source required. Simulation of a 64/spl times/20-bit DRAM in TSMC 0.18-/spl mu/m technology is provided, along with comparison to the conventional DRAM architectures.

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.

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 categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.805
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0010.002
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 head0.052
GPT teacher head0.304
Teacher spread0.252 · 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