Recurrent Neural Networks Analysis for Embedded Systems

Gonçalo Fontes Neves; Jean-Baptiste Chaudron; Arnaud Dion

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Paper

Recurrent Neural Networks Analysis for Embedded Systems

Topics: Complex Artificial Neural Network Based Systems and Dynamics; Deep Learning; Neural Approaches and Neural Architectures; Neural based Implementation, Applications and Solutions; Recurrent Neural Networks

In Proceedings of the 13th International Joint Conference on Computational Intelligence - Volume 1: NCTA, 374-383, 2021

Authors: Gonçalo Fontes Neves ; Jean-Baptiste Chaudron and Arnaud Dion

Affiliation: ISAE-SUPAERO, Université de Toulouse, France

Keyword(s): Deep Learning, RNN, GRU, LSTM, Embedded Systems.

Abstract: Artificial Neural Networks (ANNs) are biologically inspired algorithms especially efficient for pattern recognition and data classification. In particular, Recurrent Neural Networks (RNN) are a specific type of ANNs which model and process sequences of data that have temporal relationship. Thus, it introduces interesting behavior for embedded systems applications such as autopilot systems. However, RNNs (and ANNs in general) are computationally intensive algorithms, especially to allow the network to learn. This implies a wise integration and proper analysis on the embedded systems that we gather these functionalities. We present in this paper an analysis of two types of Recurrent Neural Networks, Long-Short Term Memory (LSTM) and Gated-Recurrent Unit (GRU), explain their architectures and characteristics. We propose our dedicated implementation which is tested and validated on embedded system devices with a dedicated dataset.

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Paper citation in several formats:

Fontes Neves, G. ; Chaudron, J. and Dion, A. (2021). Recurrent Neural Networks Analysis for Embedded Systems. In Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - NCTA; ISBN 978-989-758-534-0; ISSN 2184-3236, SciTePress, pages 374-383. DOI: 10.5220/0010715700003063

@conference{ncta21,
author={Gon\c{c}alo {Fontes Neves} and Jean{-}Baptiste Chaudron and Arnaud Dion},
title={Recurrent Neural Networks Analysis for Embedded Systems},
booktitle={Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - NCTA},
year={2021},
pages={374-383},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010715700003063},
isbn={978-989-758-534-0},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - NCTA
TI - Recurrent Neural Networks Analysis for Embedded Systems
SN - 978-989-758-534-0
IS - 2184-3236
AU - Fontes Neves, G.
AU - Chaudron, J.
AU - Dion, A.
PY - 2021
SP - 374
EP - 383
DO - 10.5220/0010715700003063
PB - SciTePress