  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.
