Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference - Jacob, Benoit; Kligys, Skirmantas; Chen, Bo; Zhu, Menglong; Tang, Matthew; Howard, Andrew; Adam, Hartwig; Kalenichenko, Dmitry (2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018)
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.