  Traditional neural architecture search (NAS) has a significant impact in
computer vision by automatically designing network architectures for various
tasks. In this paper, binarized neural architecture search (BNAS), with a
search space of binarized convolutions, is introduced to produce extremely
compressed models to reduce huge computational cost on embedded devices for
edge computing. The BNAS calculation is more challenging than NAS due to the
learning inefficiency caused by optimization requirements and the huge
architecture space, and the performance loss when handling the wild data in
various computing applications. To address these issues, we introduce operation
space reduction and channel sampling into BNAS to significantly reduce the cost
of searching. This is accomplished through a performance-based strategy that is
robust to wild data, which is further used to abandon less potential
operations. Furthermore, we introduce the Upper Confidence Bound (UCB) to solve
1-bit BNAS. Two optimization methods for binarized neural networks are used to
validate the effectiveness of our BNAS. Extensive experiments demonstrate that
the proposed BNAS achieves a comparable performance to NAS on both CIFAR and
ImageNet databases. An accuracy of $96.53\%$ vs. $97.22\%$ is achieved on the
CIFAR-10 dataset, but with a significantly compressed model, and a $40\%$
faster search than the state-of-the-art PC-DARTS. On the wild face recognition
task, our binarized models achieve a performance similar to their corresponding
full-precision models.
