Poster De Conférence Année : 2025

Timing Prediction of Deep Neural Networks on Multi-Core Platforms with Memory Hierarchy Effects

Résumé

In the domain of edge computing, the implementa- tion of Deep Neural Networks (DNNs) on small, resources-limited platforms is difficult. In order to optimize the inference of such systems, performance estimation is required. In that context, we propose a novel evaluation flow for mapping a DNN on embedded multi-core CPU devices. Our main contribution is to take into consideration the effect of a complex memory hierarchy on the computation time. The proposed framework is based on a SystemC simulation and calibrated with samples collected on a prototype.

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hal-05071279 , version 1 (10-06-2025)

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  • HAL Id : hal-05071279 , version 1

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Antoine Cantin, Sébastien Le Nours, Sébastien Pillement, Domenik Helms, Kim Grüttner, et al.. Timing Prediction of Deep Neural Networks on Multi-Core Platforms with Memory Hierarchy Effects. Colloque du GDR SOC2, Jun 2025, Lorient, France. pp.sciencesconf.org:gdr-soc2-2025:640949, 2025. ⟨hal-05071279⟩
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