Quentin Dariol, Sebastien Le Nours, Sebastien Pillement, Ralf Stemmer, Kim Grüttner, Domenik Helms
15ème Colloque National du GDR SOC2
Evaluation of performance for complex applica-tions such as Artificial Intelligence (AI) algorithms and morespecifically neural networks on Multi-Processor Systems on aChip (MPSoC) is tedious. Mechanisms such as data-dependentpaths and communication bus congestion induce execution timevariation, which is hard to predict accurately using traditionalanalysis methods. This paper illustrates our proposed perfor-mance prediction workflow based on simulation models forprobabilistic timing prediction for MPSoC. We aim to extend ourexisting approach to optimize neural network implementation onresource-constrained multiprocessor platforms.
Jun / 2021
PETA-MC Probabilistic Energy and Timing Analysis of Data Flow Applications on Multi-Core Processors