The high performance of the state-of-the-art deep neural networks (DNNs) is acquired at the cost of huge consumption of computing resources. Quantization of networks is recently recognized as a promising solution to solve the problem and significantly reduce the resource usage. However, the previous quantization works have mostly focused on the DNN inference, and there were very few works to address on the challenges of DNN training. In this paper, we leverage dynamic fixed-point (DFP) quantization algorithm and stochastic rounding (SR) strategy to develop a fully quantized 8-bit neural networks targeting low bitwidth training. The experiments show that, in comparison to the full-precision networks, the accuracy drop of our quantized convolutional neural networks (CNNs) can be less than 2%, even when applied to deep models evaluated on ImageNet dataset. Additionally, our 8-bit GNMT translation network can achieve almost identical BLEU to full-precision network. We further implement a prototype on FPGA and the synthesis shows that the low bitwidth training scheme can reduce the resource usage significantly.