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Record W1617123517 · doi:10.1109/pacrim.1999.799534

Embedded memory in FPGAs: recent research results

2003· article· en· W1617123517 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEmbedded Systems Design Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsField-programmable gate arrayComputer scienceComputer architectureInterconnectionEmbedded systemArchitectureSemiconductor memoryMemory architectureElectronic circuitComputer hardwareEngineeringElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

Recent dramatic improvements in integrated circuit fabrication technology have led to field-programmable gate arrays (FPGAs) capable of implementing entire digital systems. Unlike the smaller circuits that have traditionally been targeted to FPGAs, these large systems often contain memory. Architectural support for the efficient implementation of memory in next-generation FPGAs is therefore crucial. In this paper, we will describe recent research into FPGA memory architectures. We seek to uncover not only the best architecture for the memory arrays themselves, but the best architecture for their interconnection, and the interconnection of the memory architecture to the rest of the FPGA. We also show how memory arrays that are not used to implement storage can be used to implement the logic parts of circuits very efficiently. Many of the early research results have already been used in commercial FPGAs; others are likely to be used in the future.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.835
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.128
GPT teacher head0.383
Teacher spread0.255 · 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

Quick stats

Citations11
Published2003
Admission routes1
Has abstractyes

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