  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.
