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Record W2013594929 · doi:10.1145/2684746.2689083

Design Space Exploration of L1 Data Caches for FPGA-Based Multiprocessor Systems

2015· article· en· W2013594929 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
TopicParallel Computing and Optimization Techniques
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputer scienceMicroBlazeMultiprocessingField-programmable gate arrayEmbedded systemMulti-core processorCacheDesign space explorationComputer architectureCache coherenceParallel computingOperating systemCPU cacheCache algorithms

Abstract

fetched live from OpenAlex

Combining multi-processing with the high level of configurability possible with FPGA-based soft-processors, this paper presents a multiprocessing framework based on the MicroBlaze soft-processor that provides multicore support and fully coherent, independently configurable Level 1 Caches with Linux multicore support. This architecture allows for fine-grain configurability of the system, allowing for FPGA resources to be better optimized for a specific embedded application. We use our framework to explore the L1 Data Cache configuration, developing a metric for efficiency based on resource usage and static application runtime. We find that a Pseudo-Random replacement policy is consistently the more efficient choice for FPGA systems.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.250
Threshold uncertainty score0.305

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.306
GPT teacher head0.343
Teacher spread0.037 · 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