  In this paper, we propose Efficient Progressive Neural Architecture Search
(EPNAS), a neural architecture search (NAS) that efficiently handles large
search space through a novel progressive search policy with performance
prediction based on REINFORCE~\cite{Williams.1992.PG}. EPNAS is designed to
search target networks in parallel, which is more scalable on parallel systems
such as GPU/TPU clusters. More importantly, EPNAS can be generalized to
architecture search with multiple resource constraints, \eg, model size,
compute complexity or intensity, which is crucial for deployment in widespread
platforms such as mobile and cloud. We compare EPNAS against other
state-of-the-art (SoTA) network architectures (\eg,
MobileNetV2~\cite{mobilenetv2}) and efficient NAS algorithms (\eg,
ENAS~\cite{pham2018efficient}, and PNAS~\cite{Liu2017b}) on image recognition
tasks using CIFAR10 and ImageNet. On both datasets, EPNAS is superior \wrt
architecture searching speed and recognition accuracy.
