Recent breakthroughs in Neural Architectural Search (NAS) have achieved
state-of-the-art performance in many tasks such as image classification and
language understanding. However, most existing works only optimize for model
accuracy and largely ignore other important factors imposed by the underlying
hardware and devices, such as latency and energy, when making inference. In
this paper, we first introduce the problem of NAS and provide a survey on
recent works. Then we deep dive into two recent advancements on extending NAS
into multiple-objective frameworks: MONAS and DPP-Net. Both MONAS and DPP-Net
are capable of optimizing accuracy and other objectives imposed by devices,
searching for neural architectures that can be best deployed on a wide spectrum
of devices: from embedded systems and mobile devices to workstations.
Experimental results are poised to show that architectures found by MONAS and
DPP-Net achieves Pareto optimality w.r.t the given objectives for various
devices.

本文介绍了神经架构搜索的问题，并调研了最近的研究成果。然后，我们深入探讨了两项对 NAS 进行多目标框架扩展的最新进展：MONAS 和 DPP-Net。MONAS 和 DPP-Net 都能够优化精度和设备带入的其他目标，寻找可以在广泛设备上最好部署的神经架构。实验结果显示，MONAS 和 DPP-Net 找到的架构在各种设备上均达到了 Pareto 最优性。