  Binary Convolutional Neural Networks (CNNs) have significantly reduced the
number of arithmetic operations and the size of memory storage needed for CNNs,
which makes their deployment on mobile and embedded systems more feasible.
However, the CNN architecture after binarizing requires to be redesigned and
refined significantly due to two reasons: 1. the large accumulation error of
binarization in the forward propagation, and 2. the severe gradient mismatch
problem of binarization in the backward propagation. Even though the
substantial effort has been invested in designing architectures for single and
multiple binary CNNs, it is still difficult to find an optimal architecture for
binary CNNs. In this paper, we propose a strategy, named NASB, which adopts
Neural Architecture Search (NAS) to find an optimal architecture for the
binarization of CNNs. Due to the flexibility of this automated strategy, the
obtained architecture is not only suitable for binarization but also has low
overhead, achieving a better trade-off between the accuracy and computational
complexity of hand-optimized binary CNNs. The implementation of NASB strategy
is evaluated on the ImageNet dataset and demonstrated as a better solution
compared to existing quantized CNNs. With the insignificant overhead increase,
NASB outperforms existing single and multiple binary CNNs by up to 4.0% and
1.0% Top-1 accuracy respectively, bringing them closer to the precision of
their full precision counterpart. The code and pretrained models will be
publicly available.
