%0 Conference Paper %1 8578384 %A Jacob, B. %A Kligys, S. %A Chen, B. %A Zhu, M. %A Tang, M. %A Howard, A. %A Adam, H. %A Kalenichenko, D. %B 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition %D 2018 %K convnets dnn quantization %P 2704-2713 %R 10.1109/CVPR.2018.00286 %T Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference %U https://ieeexplore.ieee.org/document/8578384 %X 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.

@inproceedings{8578384, abstract = {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.}, added-at = {2020-10-23T21:03:58.000+0200}, author = {{Jacob}, B. and {Kligys}, S. and {Chen}, B. and {Zhu}, M. and {Tang}, M. and {Howard}, A. and {Adam}, H. and {Kalenichenko}, D.}, biburl = {https://www.bibsonomy.org/bibtex/262a0538e9b21d2a374a33133a4043e89/sohnki}, booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition}, description = {Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference - IEEE Conference Publication}, doi = {10.1109/CVPR.2018.00286}, interhash = {2c9fd8218b7dacf6236524a3a826e98e}, intrahash = {62a0538e9b21d2a374a33133a4043e89}, issn = {2575-7075}, keywords = {convnets dnn quantization}, month = {June}, pages = {2704-2713}, timestamp = {2020-10-23T21:03:58.000+0200}, title = {Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference}, url = {https://ieeexplore.ieee.org/document/8578384}, year = 2018 }