  Memory and computation efficient deep learning architec- tures are crucial to
continued proliferation of machine learning capabili- ties to new platforms and
systems. Binarization of operations in convo- lutional neural networks has
shown promising results in reducing model size and computing efficiency. In
this paper, we tackle the problem us- ing a strategy different from the
existing literature by proposing local binary pattern networks or LBPNet, that
is able to learn and perform binary operations in an end-to-end fashion.
LBPNet1 uses local binary comparisons and random projection in place of
conventional convolu- tion (or approximation of convolution) operations. These
operations can be implemented efficiently on different platforms including
direct hard- ware implementation. We applied LBPNet and its variants on
standard benchmarks. The results are promising across benchmarks while provid-
ing an important means to improve memory and speed efficiency that is
particularly suited for small footprint devices and hardware accelerators.
