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Rapport (Rapport Technique) Année : 2022

Setup of an Experimental Framework for Performance Modeling and Prediction of Embedded Multicore AI Architectures

Résumé

Evaluation of performance for complex applications such as Artificial Intelligence (AI) algorithms and more specifically neural networks on Multi-Processor Systems on a Chip (MPSoC) is tedious. Finding an optimized partitioning of the application while predicting accurately the latency induced by communication bus congestion, is hard using traditional analysis methods. This document presents a performance prediction workflow based on SystemC simulation models for timing prediction of neural networks on MPSoC.
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Dates et versions

hal-03546804 , version 1 (28-01-2022)

Identifiants

  • HAL Id : hal-03546804 , version 1

Citer

Quentin Dariol, Sebastien Le Nours, Sébastien Pillement, Kim Grüttner, Domenik Helms, et al.. Setup of an Experimental Framework for Performance Modeling and Prediction of Embedded Multicore AI Architectures. [Technical Report] IETR UMR 6164. 2022. ⟨hal-03546804⟩
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