The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Quantized Convolutional Neural Networks for Mobile Devices
Open accessAbstract
Recently, convolutional neural networks (CNN) have demonstrated impressive performance in various computer vision tasks. However, high performance hardware is typically indispensable for the application of CNN models due to the high computation complexity, which prohibits their further extensions. In this paper, we propose an efficient framework, namely Quantized CNN, to simultaneously speed-up the computation and reduce the storage and memory overhead of CNN models. Both filter kernels in convolutional layers and weighting matrices in fully-connected layers are quantized, aiming at minimizing the estimation error of each layer's response. Extensive experiments on the ILSVRC-12 benchmark demonstrate 4 ~ 6× speed-up and 15 ~ 20× compression with merely one percentage loss of classification accuracy. With our quantized CNN model, even mobile devices can accurately classify images within one second.
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Citations
Cites
ImageNet classification with deep convolutional neural networks
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Cited by
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
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Metadata
- Authors
- Wu, J., Leng, C., Wang, Y., Hu, Q., & Cheng, J.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 4820-4828
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2233116163
- Cited by
- 1,212
- References
- 12
- FWCI
- 41.11
- Type
- conference paper
- Language
- en
- Open access
- green
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