Quantization of a neural network has an inherent problem called accumulated quantization error, which is the key obstacle towards ultra-low precision, e.g., 2- or 3-bit precision. To resolve this problem, we propose precision highway, which forms an end-to-end high-precision information flow while performing the ultra-low-precision computation. First, we describe how the precision highway reduce the accumulated quantization error in both convolutional and recurrent neural networks. We also provide the quantitative analysis of the benefit of precision highway and evaluate the overhead on the state-of-the-art hardware accelerator. In the experiments, our proposed method outperforms the best existing quantization methods while offering 3-bit weight/activation quantization with no accuracy loss and 2-bit quantization with a 2.45 % top-1 accuracy loss in ResNet-50. We also report that the proposed method significantly outperforms the existing method in the 2-bit quantization of an LSTM for language modeling.
precision highway; a generalized concept of high-precision information flow for sub 4-bit quantization  Investigates the problem of neural network quantization by employing an end-to-end precision highway to reduce the accumulated quantization error and enable ultra-low precision in deep neural networks.  This paper studies methods to improve the performance of quantized neural networks This paper proposes to keep a high activation/gradient flow in two kinds of networks structures, ResNet and LSTM.