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Record W2027069801 · doi:10.1145/2145694.2145730

Multi-ported memories for FPGAs via XOR

2012· article· en· W2027069801 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPortingField-programmable gate arrayComputer scienceLogic blockBlock (permutation group theory)XOR gateLookup tableExploitParallel computingEmbedded systemArithmeticComputer hardwareLogic gateAlgorithmOperating systemMathematics

Abstract

fetched live from OpenAlex

Multi-ported memories are challenging to implement with FPGAs since the block RAMs included in the fabric typically have only two ports. Any design that requires a memory with more than two ports must therefore be built out of logic elements or by combining multiple block RAMs. The recently-proposed Live Value Table (LVT) design provides a significant operating frequency improvement over conventional approaches. In this paper we present an alternative approach based on the XOR operation that provides multi-ported memories that use far less logic but more block RAMs than LVT designs, and are often smaller and faster for memories that are more than 512 entries deep. We show that (i) both designs can exploit multipumping to trade speed for area savings, (ii) that multipumped XOR designs are significantly smaller but moderately slower than their LVT counterparts, and (iii) that both the LVT and XOR approaches are valuable and useful in different situations, depending on the constraints and resource utilization of the enclosing design.

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)0.000
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: Methods
Teacher disagreement score0.933
Threshold uncertainty score0.292

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 head0.037
GPT teacher head0.298
Teacher spread0.261 · 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