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)
2016-06
会议名称IEEE Conference on Computer Vision and Pattern Recognition
会议日期2016-6
会议地点Las Vegas, U.S.
摘要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 CNNmodel, even mobile devices can accurately classify images within one second.
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/14970
专题模式识别国家重点实验室_图像与视频分析
作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Wu JX,Leng C,Wang YH,et al. Quantized Convolutional Neural Networks for Mobile Devices[C],2016.
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