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

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
Probabilistic Energy and Timing Analysis of Data Flow Applications on Multi-Core Processors