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Poster communications

A Measurement-based Performance Evaluation Framework for Neural Networks on MPSoCs

Abstract : 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. Mechanisms such as data-dependent paths and communication bus congestion induce execution time variation, which is hard to predict accurately using traditional analysis methods. This paper illustrates our proposed performance prediction workflow based on simulation models for probabilistic timing prediction for MPSoC. We aim to extend our existing approach to optimize neural network implementation on resource-constrained multiprocessor platforms.
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Poster communications
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https://hal.archives-ouvertes.fr/hal-03248152
Contributor : Sandrine Charlier Connect in order to contact the contributor
Submitted on : Monday, June 14, 2021 - 9:51:50 AM
Last modification on : Wednesday, April 27, 2022 - 4:03:14 AM
Long-term archiving on: : Thursday, September 16, 2021 - 8:21:15 AM

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  • HAL Id : hal-03248152, version 1

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Quentin Dariol, Sébastien Le Nours, Sébastien Pillement, Ralf Stemmer, Kim Grüttner, et al.. A Measurement-based Performance Evaluation Framework for Neural Networks on MPSoCs. 15ème Colloque National du GDR SOC2, Jun 2021, Rennes, France. 2021. ⟨hal-03248152⟩

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