Early Performance and Energy Prediction of Neural Networks Deployed on Multi-Core Platforms - Archive ouverte HAL
Poster De Conférence Année : 2023

Early Performance and Energy Prediction of Neural Networks Deployed on Multi-Core Platforms

Résumé

Early evaluation of Neural Networks (NN) deployments on multi-core platforms is necessary to find deployments that optimize resource usage, performance and energy. In this paper, we propose a timing and power modeling methodology which combines simulation, analytical models, and measurements to offer fast yet accurate performance and energy prediction of NN deployments on multi-core platforms. The proposed approach is validated against measurements obtained from a real implementation of 27 mappings of four NNs with high accuracy and a fast evaluation time of approximatively 20 s per mapping.

Domaines

Electronique
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Dates et versions

hal-04186902 , version 1 (24-08-2023)

Identifiants

  • HAL Id : hal-04186902 , version 1

Citer

Quentin Dariol, Sébastien Le Nours, Sébastien Pillement, Ralf Stemmer, Domenik Helms, et al.. Early Performance and Energy Prediction of Neural Networks Deployed on Multi-Core Platforms. GRETSI 2023 XXIXème Colloque Francophone de Traitement du Signal et des Images, Aug 2023, GRENOBLE, France. 2023-08, pp.ID PAPER 1144. ⟨hal-04186902⟩
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