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Record W2106668017 · doi:10.1109/ccece.2009.5090313

HW/SW co-design architecture exploration for VLSI maze routing

2009· article· en· W2106668017 on OpenAlexaff
Mahdi Elghazali, Ahmed Elhossini, Shawki Areibi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMicroBlazeComputer scienceField-programmable gate arrayEmbedded systemRouting (electronic design automation)FIFO (computing and electronics)Very-large-scale integrationComputer hardwareComputer architectureLookup tableOperating system

Abstract

fetched live from OpenAlex

The advance in FPGA technology allowed embedding different types of resources on a single chip. These resources range from a simple look-up table to a complete processor. The resources available on the FPGA fabric allow building various hardware systems for different applications with several trade-offs in terms of performance and power consumption. This paper proposes six different architectures to implement VLSI maze routing algorithm on FPGAs. These architectures utilize two processors (MicroBlaze and Power-PC) and a separate hardware accelerator. The hardware accelerator was designed for the maze routing algorithm with two different protocols for data transfer. All architectures are evaluated based on seven benchmarks. The evaluation includes the performance and the power consumption of the architectures. This work demonstrate that the architecture composed of a soft-core processor directly connected to the hardware accelerator with fully utilized FIFO channel achieves the best power-delay product among all the investigated 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.

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 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 score0.919
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.049
GPT teacher head0.281
Teacher spread0.232 · 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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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