Deep neural networks (DNNs) have been considered to be the state-of-the-art artificial intelligence methods in a very broad range of applications. However, DNNs are compute intensive and memory intensive which are difficult to be employed in practical scenarios. Due to their favorable parallel computing ability, a series of DNN accelerators have been proposed. However, the improvement of on-chip computing capacity and the increasing number of parameters in the neural networks make access to memory a bottleneck. In this paper, we analyze the existing DNN algorithms. We observe that the special structure of neural networks makes it have two useful characteristics, which are unilateral directivity and local independence. Based on these characteristics, we propose a general software scheduling method to reduce memory access cost. Based on the experimental results, our method can reduce 32% memory access cost and achieve a speedup of 1.6x in average on our experiment platform and the best result is in ResNet-50, which is up to 56% and 2.62x.