  Binary neural networks have attracted tremendous attention due to the
efficiency for deploying them on mobile devices. Since the weak expression
ability of binary weights and features, their accuracy is usually much lower
than that of full-precision (i.e. 32-bit) models. Here we present a new frame
work for automatically searching for compact but accurate binary neural
networks. In practice, number of channels in each layer will be encoded into
the search space and optimized using the evolutionary algorithm. Experiments
conducted on benchmark datasets and neural architectures demonstrate that our
searched binary networks can achieve the performance of full-precision models
with acceptable increments on model sizes and calculations.
