Hybrid Approach for Efficient Quantization of Weights in Convolutional Neural Networks
Sanghyun Seo, Juntae Kim · 2018
Convolutional neural networks(CNN) have achieved outstanding results in the fields of image recognition which classifies objects in the input images. In the deep neural networks such as CNN, the number of layers and the number of neurons in each layer are large. In other words, the deep neural networks requires relatively large storage space and calculation process. However, in embedded devices for object recognition in autonomous vehicles, large storage space and high computational complexity are constraints. For this reasons, various methodologies have been proposed to apply CNN to small embedded hardware such as mobile devices, FPGA and ASIC efficiently. In this paper, we quantize the weights of AlexNet without a large drop in accuracy by using a hybrid quantizer using uniform quantizer and k-means clustering.