  Neural Architecture Search (NAS) has proved effective in offering
outperforming alternatives to handcrafted neural networks. In this paper we
analyse the benefits of NAS for image classification tasks under strict
computational constraints. Our aim is to automate the design of highly
efficient deep neural networks, capable of offering fast and accurate
predictions and that could be deployed on a low-memory, low-power
system-on-chip. The task thus becomes a three-party trade-off between accuracy,
computational complexity, and memory requirements. To address this concern, we
propose Multi-Scale Resource-Aware Neural Architecture Search (MS-RANAS). We
employ a one-shot architecture search approach in order to obtain a reduced
search cost and we focus on an anytime prediction setting. Through the usage of
multiple-scaled features and early classifiers, we achieved state-of-the-art
results in terms of accuracy-speed trade-off.
