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.

该研究提出了一种名为 EPNAS（高效渐进的神经结构搜索）的神经结构搜索方法，它通过一种新的渐进搜索策略和基于 REINFORCE 的性能预测有效地处理大型搜索空间。EPNAS 被设计成能够在并行系统（如 GPU/TPU 集群）上并行搜索目标网络。该方法还可以推广到具有多个资源限制的体系结构搜索，这对于在移动设备和云平台等广泛平台上进行部署非常重要。在使用 CIFAR10 和 ImageNet 进行图像识别任务时，该方法在架构搜索速度和识别准确性方面均优于先进的网络结构和高效的 NAS 算法。