Quantized Convolutional Neural Networks for Mobile Devices
文献类型：会议论文
| 作者 | Wu JX(吴家祥) ; Leng C(冷聪); Wang YH(王宇航); Hu QH(胡庆浩); Cheng J(程健)
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| 出版日期 | 2016-06 |
| 会议日期 | 2016-6 |
| 会议地点 | Las Vegas, U.S. |
| 英文摘要 | 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/14970]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
| 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]. 见:. Las Vegas, U.S.. 2016-6. |
入库方式： OAI收割
来源：自动化研究所
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