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Record W2775294075 · doi:10.1109/pacrim.2017.8121892

Data-flow implementation of concurrent asynchronous systems

2017· article· en· W2775294075 on OpenAlexaff
Fayez Gebali, Ali Alzahrani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceAsynchronous communicationCorrectnessDistributed computingEmbedded systemScheduling (production processes)Synchronization (alternating current)Data flow diagramModel of computationMulti-core processorComputationParallel computingComputer networkChannel (broadcasting)

Abstract

fetched live from OpenAlex

Embedded multi core systems are implemented as systems-on-chip (SoC) that rely on packet store-and-forward networks-on-chip (NoC) for communications. These systems do not use busses nor global clock. Instead routers are used to move data between the cores and each core uses its own local clock. This implies concurrent asynchronous computing. Implementing algorithms in such system is very much facilitated using event-driven concepts. In this work, we propose an event-driven hardware computational model that is more suitable to describe, simulate, and design concurrent asynchronous systems. The proposed model of computation has advantages such as flexible I/O timing in term of scheduling policy, processing wise execute as soon as possible, and self timed event driven system. In other words, I/O timing and correctness of algorithm evaluation are dissociated in this work. The main advantage of this proposal is ability to obfuscate algorithm evaluation to thwart side-channel attacks which has important implications for cryptographic applications.

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: Empirical · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.336

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 communication0.0000.001
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 head0.068
GPT teacher head0.352
Teacher spread0.284 · 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
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

Citations1
Published2017
Admission routes1
Has abstractyes

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