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Record W2900224194 · doi:10.1109/mm.2018.2877288

Accelerators and Coherence: An SoC Perspective

2018· article· en· W2900224194 on OpenAlexfundno aff
Davide Giri, Paolo Mantovani, Luca P. Carloni

Bibliographic record

VenueIEEE Micro · 2018
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsnot available
FundersNational Science Foundation of Sri LankaDefense Advanced Research Projects AgencyMinistère de l'Économie, de la Science et de l'Innovation - QuébecUniversity of Illinois at ChicagoPolitecnico di Torino
KeywordsComputer scienceComputer architectureMemory hierarchySystem on a chipEmbedded systemCoherence (philosophical gambling strategy)Variety (cybernetics)Perspective (graphical)Isolation (microbiology)Operating system

Abstract

fetched live from OpenAlex

The complexity of System-on-Chip (SoC) designs continues to grow as each SoC features an increasing variety of loosely coupled accelerators together with multiple processor cores. Specialized-hardware accelerators are typically designed in isolation, optimized for the algorithm they are implementing, and with limited consideration of the implications of their integration into a given SoC. However, the interaction between these accelerators and the memory hierarchy is critically important for their performance and the performance of the overall SoC. By leveraging our platform for rapid SoC prototyping, we analyze three models of coherence for loosely coupled accelerators from a system-level perspective.

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)0.003
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: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.023
GPT teacher head0.297
Teacher spread0.274 · 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
GenreEmpirical

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

Citations33
Published2018
Admission routes1
Has abstractyes

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