  This paper describes a CNN where all CNN style 2D convolution operations that
lower to matrix matrix multiplication are fully binary. The network is derived
from a common building block structure that is consistent with a constructive
proof outline showing that binary neural networks are universal function
approximators. 71.24% top 1 accuracy on the 2012 ImageNet validation set was
achieved with a 2 step training procedure and implementation strategies
optimized for binary operands are provided.
