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Record W2130886211 · doi:10.1109/iwsoc.2005.46

Component-based methodology for hardware design of a dataflow processing network

2005· article· en· W2130886211 on OpenAlexaff
R. Grou-Szabo, H. Ghattas, Yvon Savaria, Gabriela Nicolescu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEmbedded Systems Design Techniques
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsDataflowComputer scienceNAND gateFIFO (computing and electronics)Component (thermodynamics)Embedded systemDesign flowComputer hardwareRouting (electronic design automation)CMOSData-flow analysisNAND logicData flow diagramComputer architectureLogic gateParallel computingEngineeringElectronic engineeringAlgorithm

Abstract

fetched live from OpenAlex

This paper proposes a new methodology for the design of reusable IP blocks used in data-flow architectures. These IP blocks are elaborated by encapsulating individual operators that constitute part of an algorithm within wrappers that possess a configurable communication layer. This IPs communicates using the VCI protocol, and the interconnections are automatically generated from a data flow graph. The interface wrapper has been designed and simulated in 0.18 /spl mu/m CMOS technology. When implemented using 2 10-bit input ports, a 12-bit output port and a FIFO depth of 8, synthesis results show that the circuit has a gate count of 1730 NAND gates with a maximum operating frequency of 400 MHz before placement and routing.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.174
GPT teacher head0.352
Teacher spread0.178 · 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 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".

Quick stats

Citations2
Published2005
Admission routes1
Has abstractyes

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