  Recent advances show that Neural Architectural Search (NAS) method is able to
find state-of-the-art image classification deep architectures. In this paper,
we consider the one-shot NAS problem for resource constrained applications.
This problem is of great interest because it is critical to choose different
architectures according to task complexity when the resource is constrained.
Previous techniques are either too slow for one-shot learning or does not take
the resource constraint into consideration. In this paper, we propose the
resource constrained differentiable architecture search (RC-DARTS) method to
learn architectures that are significantly smaller and faster while achieving
comparable accuracy. Specifically, we propose to formulate the RC-DARTS task as
a constrained optimization problem by adding the resource constraint. An
iterative projection method is proposed to solve the given constrained
optimization problem. We also propose a multi-level search strategy to enable
layers at different depths to adaptively learn different types of neural
architectures. Through extensive experiments on the Cifar10 and ImageNet
datasets, we show that the RC-DARTS method learns lightweight neural
architectures which have smaller model size and lower computational complexity
while achieving comparable or better performances than the state-of-the-art
methods.
