  Neural Architecture Search (NAS) is a popular tool for automatically
generating Neural Network (NN) architectures. In early NAS works, these tools
typically optimized NN architectures for a single metric, such as accuracy.
However, in the case of resource constrained Machine Learning, one single
metric is not enough to evaluate a NN architecture. For example, a NN model
achieving a high accuracy is not useful if it does not fit inside the flash
memory of a given system. Therefore, recent works on NAS for resource
constrained systems have investigated various approaches to optimize for
multiple metrics. In this paper, we propose that, on top of these approaches,
it could be beneficial for NAS optimization of resource constrained systems to
also consider input data granularity. We name such a system "Data Aware NAS",
and we provide experimental evidence of its benefits by comparing it to
traditional NAS.
