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