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 $4 \sim 6 \times$ speed-up and $15 \sim 20 \times$ compression with merely one percentage loss of classification accuracy. With our quantized CNN model, even mobile devices can accurately classify images within one second.

该研究提出了一种名为量化卷积神经网络的模型，旨在通过量化卷积层中的滤波器核和全连接层中的权重矩阵，实现计算效率的提升和存储内存开销的降低，相对于非量化模型，该模型在ILSVRC-12基准测试中达到4~6倍的加速和15~20倍的压缩，仅有1%左右的分类准确率损失，并且甚至可以在移动设备上在一秒内精准分类照片。

移动设备上的量化卷积神经网络