Memory Efficient Nonuniform Quantization for Deep Convolutional Neural Network

Convolutional neural network (CNN) is one of the most famous algorithms for deep learning. It has been applied in various applications due to its remarkable performance. The real-time hardware implement of CNN is highly demanded due to its excellent performance in computer vision. However, the cost of memory of a deep CNN is very huge which increases the area of hardware implementation. In this paper, we apply several methods in the quantization of CNN and use about 5 bits for convolutional layers. The accuracy lost is less than $2\%$ without fine tuning. Our experiment is depending on the VGG-16 net and Alex net. In VGG-16 net, the total memory needed after uniform quantization is 16.85 MB per image and the total memory needed after our quantization is only about 8.42 MB. Our quantization method has saved $50.0\%$ of the memory needed in VGG-16 and Alex net compared with the state-of-art quantization method.