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Record W2547022751 · doi:10.1109/ccece.2016.7726823

Rapid design space exploration of multi-clock domain MPSoCs with hybrid prototyping

2016· article· en· W2547022751 on OpenAlexaff
Ehsan Saboori, Samar Abdi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsMPSoCEmbedded systemEmulationComputer scienceRapid prototypingField-programmable gate arrayDesign space explorationSoftware prototypingVirtual prototypingSoftwareHardware emulationComputer architectureSystem on a chipEngineeringSoftware developmentSimulation

Abstract

fetched live from OpenAlex

This paper presents novel techniques of using hybrid prototyping for early power-performance analysis of MPSoC designs with multiple clock domains. The fundamental idea of hybrid prototyping is to simulate a design with multiple cores by creating an emulation kernel in software on top of a single physical instance of the core. However, so far hybrid prototyping has been limited to homogeneous multicores running at the same clock frequency. Moreover, hybrid prototyping has not yet been demonstrated for efficient design space exploration. Our work focuses on enhancing the capabilities of hybrid prototyping, such that it can be applied to realistic multi-clock MPSoC designs as well to perform early power-performance evaluation of MPSoC designs. Our experiments using industrial strength applications such as JPEG, MP3 and Packet Processing, demonstrate the high accuracy of our hybrid prototypes, and over two orders of magnitude improvement over software simulation speed. We also demonstrate that exploring over 150 design options using hybrid prototyping can be done with high reliability in the order of minutes compared to multiple days using conventional FPGA prototyping.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.575
Threshold uncertainty score0.266

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 science0.0000.000
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 head0.050
GPT teacher head0.259
Teacher spread0.209 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations2
Published2016
Admission routes1
Has abstractyes

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