CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Quantized Convolutional Neural Networks for Mobile Devices
Wu JX(吴家祥); Leng C(冷聪); Wang YH(王宇航); Hu QH(胡庆浩); Cheng J(程健)
Conference NameIEEE Conference on Computer Vision and Pattern Recognition
Conference Date2016-6
Conference PlaceLas Vegas, U.S.
AbstractRecently, 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 CNNmodel, even mobile devices can accurately classify images within one second.
Document Type会议论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wu JX,Leng C,Wang YH,et al. Quantized Convolutional Neural Networks for Mobile Devices[C],2016.
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