  Many applications of mobile deep learning, especially real-time computer
vision workloads, are constrained by computation power. This is particularly
true for workloads running on older consumer phones, where a typical device
might be powered by a single- or dual-core ARMv7 CPU. We provide an open-source
implementation and a comprehensive analysis of (to our knowledge) the state of
the art ultra-low-precision (<4 bit precision) implementation of the core
primitives required for modern deep learning workloads on ARMv7 devices, and
demonstrate speedups of 4x-20x over our additional state-of-the-art float32 and
int8 baselines.
