In this paper we study the real time deployment of deep learning algorithms in low resource computational environments. As the use case, we compare the accuracy and speed of neural networks for smile detection using different neural network architectures and their system level implementation on NVidia Jetson embedded platform. We also propose an asynchronous multithreading scheme for parallelizing the pipeline. Within this framework, we experimentally compare thirteen widely used network topologies. The experiments show that low complexity architectures can achieve almost equal performance as larger ones, with a fraction of computation required.
@article{arxiv.1807.10570,
title = {Embedded Implementation of a Deep Learning Smile Detector},
author = {Pedram Ghazi and Antti P. Happonen and Jani Boutellier and Heikki Huttunen},
journal= {arXiv preprint arXiv:1807.10570},
year = {2018}
}
Comments
This work has been submitted to the IEEE for possible publication