  Neural architecture search (NAS) proves to be among the best approaches for
many tasks by generating an application-adaptive neural architecture, which is
still challenged by high computational cost and memory consumption. At the same
time, 1-bit convolutional neural networks (CNNs) with binarized weights and
activations show their potential for resource-limited embedded devices. One
natural approach is to use 1-bit CNNs to reduce the computation and memory cost
of NAS by taking advantage of the strengths of each in a unified framework. To
this end, a Child-Parent (CP) model is introduced to a differentiable NAS to
search the binarized architecture (Child) under the supervision of a
full-precision model (Parent). In the search stage, the Child-Parent model uses
an indicator generated by the child and parent model accuracy to evaluate the
performance and abandon operations with less potential. In the training stage,
a kernel-level CP loss is introduced to optimize the binarized network.
Extensive experiments demonstrate that the proposed CP-NAS achieves a
comparable accuracy with traditional NAS on both the CIFAR and ImageNet
databases. It achieves the accuracy of $95.27\%$ on CIFAR-10, $64.3\%$ on
ImageNet with binarized weights and activations, and a $30\%$ faster search
than prior arts.
