Quantized Convolutional Neural Networks for Mobile Devices
This work addresses the challenge of deploying CNNs on mobile devices by reducing resource demands, though it is incremental as it builds on existing quantization techniques.
The authors tackled the problem of high computational complexity and storage requirements of convolutional neural networks (CNNs) by proposing a quantized CNN framework, achieving 4-6x speed-up and 15-20x compression with only a 1% loss in classification accuracy on the ILSVRC-12 benchmark.
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.