  Binarized neural networks, or BNNs, show great promise in edge-side
applications with resource limited hardware, but raise the concerns of reduced
accuracy. Motivated by the complex neural networks, in this paper we introduce
complex representation into the BNNs and propose Binary complex neural network
-- a novel network design that processes binary complex inputs and weights
through complex convolution, but still can harvest the extraordinary
computation efficiency of BNNs. To ensure fast convergence rate, we propose
novel BCNN based batch normalization function and weight initialization
function. Experimental results on Cifar10 and ImageNet using state-of-the-art
network models (e.g., ResNet, ResNetE and NIN) show that BCNN can achieve
better accuracy compared to the original BNN models. BCNN improves BNN by
strengthening its learning capability through complex representation and
extending its applicability to complex-valued input data. The source code of
BCNN will be released on GitHub.
