  Binary Neural Networks (BNNs) have received significant attention due to
their promising efficiency. Currently, most BNN studies directly adopt
widely-used CNN architectures, which can be suboptimal for BNNs. This paper
proposes a novel Binary ARchitecture Search (BARS) flow to discover superior
binary architecture in a large design space. Specifically, we analyze the
information bottlenecks that are related to both the topology and layout
architecture design choices. And we propose to automatically search for the
optimal information flow. To achieve that, we design a two-level (Macro &
Micro) search space tailored for BNNs and apply a differentiable neural
architecture search (NAS) to explore this search space efficiently. The
macro-level search space includes width and depth decisions, which is required
for better balancing the model performance and complexity. We also design the
micro-level search space to strengthen the information flow for BNN. %A notable
challenge of BNN architecture search lies in that binary operations exacerbate
the "collapse" problem of differentiable NAS, for which we incorporate various
search and derive strategies to stabilize the search process. On CIFAR-10, BARS
achieves 1.5% higher accuracy with 2/3 binary operations and 1/10
floating-point operations comparing with existing BNN NAS studies. On ImageNet,
with similar resource consumption, BARS-discovered architecture achieves a 6%
accuracy gain than hand-crafted binary ResNet-18 architectures and outperforms
other binary architectures while fully binarizing the architecture backbone.
