  Designing Deep Neural Networks (DNNs) running on edge hardware remains a
challenge. Standard designs have been adopted by the community to facilitate
the deployment of Neural Network models. However, not much emphasis is put on
adapting the network topology to fit hardware constraints. In this paper, we
adapt one of the most widely used architectures for mobile hardware platforms,
MobileNetV2, and study the impact of changing its topology and applying
post-training quantization. We discuss the impact of the adaptations and the
deployment of the model on an embedded hardware platform for face detection.
