  The rising popularity of intelligent mobile devices and the daunting
computational cost of deep learning-based models call for efficient and
accurate on-device inference schemes. We propose a quantization scheme that
allows inference to be carried out using integer-only arithmetic, which can be
implemented more efficiently than floating point inference on commonly
available integer-only hardware. We also co-design a training procedure to
preserve end-to-end model accuracy post quantization. As a result, the proposed
quantization scheme improves the tradeoff between accuracy and on-device
latency. The improvements are significant even on MobileNets, a model family
known for run-time efficiency, and are demonstrated in ImageNet classification
and COCO detection on popular CPUs.
