Recurrent Neural Networks Analysis for Embedded Systems

Gonçalo Fontes Neves, Jean-Baptiste Chaudron, Arnaud Dion

2021

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.

Paper Citation

in Harvard Style

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) - Volume 1: NCTA; ISBN 978-989-758-534-0, SciTePress, pages 374-383. DOI: 10.5220/0010715700003063

in Bibtex Style

@conference{ncta21,
author={Gonçalo 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) - Volume 1: NCTA},
year={2021},
pages={374-383},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010715700003063},
isbn={978-989-758-534-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - Volume 1: NCTA
TI - Recurrent Neural Networks Analysis for Embedded Systems
SN - 978-989-758-534-0
AU - Fontes Neves G.
AU - Chaudron J.
AU - Dion A.
PY - 2021
SP - 374
EP - 383
DO - 10.5220/0010715700003063
PB - SciTePress