RT Journal Article
T1 High performance inference of gait recognition models on embedded systems.
A1 Ruiz Barroso, Paula
A1 Castro, Francisco M.
A1 Delgado-Escaño, Rubén
A1 Ramos-Cózar, Julián
A1 Guil-Mata, Nicolás
K1 Biomecánica
K1 Aprendizaje automático
AB Edge computing is gaining importance in the realm of Deep Learning, particularly after powerful devices such as recent heterogeneous embedded systems have demonstrated remarkable skills for accelerating their challenging computational requirements. In this work, we evaluate different hardware and software optimizations applied to state-of-the-art gait recognition approaches deployed on two Jetson devices with very different hardware capabilities. Specifically, we have selected three models with different characteristics in order to provide an in-depth deployment evaluation. This way, a 2D convolution-based model allows us to evaluate devices performance when a huge number of parameters must be managed. A model based on 3D convolutions is deployed to study devices capability to perform these kinds of operations. Finally, a novel model with a small number of parameters but with a huge number of activations is also evaluated. Obtained results show that different hardware and software optimizations are able to improve up to  energy consumption and  execution time w.r.t. baseline deployment, depending on the model and target device.1
PB Elsevier
YR 2022
FD 2022
LK https://hdl.handle.net/10630/38580
UL https://hdl.handle.net/10630/38580
LA eng
NO Sustainable Computing: Informatics and Systems, 36, 100814.
NO Política de acceso abierto tomada de: https://openpolicyfinder.jisc.ac.uk/id/publication/35887
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026