On-Device Image Classification with Proxyless Neural Architecture Search and Quantization-Aware Fine-Tuning
Abstract
It is challenging to efficiently deploy deep learning models on resource-constrained hardware devices (e.g., mobile and IoT devices) with strict efficiency constraints (e.g., latency, energy consumption). We employ Proxyless Neural Architecture Search (ProxylessNAS) to auto design compact and specialized neural network architectures for the target hardware platform. ProxylessNAS makes latency differentiable, so we can optimize not only accuracy but also latency by gradient descent. Such direct optimization saves the search cost by 200x compared to conventional neural architecture search methods. Our work is followed by quantization-aware fine-tuning to further boost efficiency. In the Low Power Image Recognition Competition at CVPR'19, our solution won the 3rd place on the task of Real-Time Image Classification (online track).
Cite
Text
Cai et al. "On-Device Image Classification with Proxyless Neural Architecture Search and Quantization-Aware Fine-Tuning." IEEE/CVF International Conference on Computer Vision Workshops, 2019. doi:10.1109/ICCVW.2019.00307Markdown
[Cai et al. "On-Device Image Classification with Proxyless Neural Architecture Search and Quantization-Aware Fine-Tuning." IEEE/CVF International Conference on Computer Vision Workshops, 2019.](https://mlanthology.org/iccvw/2019/cai2019iccvw-ondevice/) doi:10.1109/ICCVW.2019.00307BibTeX
@inproceedings{cai2019iccvw-ondevice,
title = {{On-Device Image Classification with Proxyless Neural Architecture Search and Quantization-Aware Fine-Tuning}},
author = {Cai, Han and Wang, Tianzhe and Wu, Zhanghao and Wang, Kuan and Lin, Ji and Han, Song},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2019},
pages = {2509-2513},
doi = {10.1109/ICCVW.2019.00307},
url = {https://mlanthology.org/iccvw/2019/cai2019iccvw-ondevice/}
}