Abstract: Deep neural networks (DNNs) have become increasingly popular as machine learning technique in applications, due to their ability to achieve high accuracy for tasks such as speech/image recognition. However, with the rapid growth on the scale of data and precision of recognition, the topology of neural network is becoming more and more complicated. Thus, how to design the energy-efficiency and programmability, neural or deep learning accelerator plays an essential role in next generation computer. In this paper, we propose a layer granularity analysis method, which could extract computation operations and memory requirement features through general expression and basic operation attributions. We also propose a max value replacement schedule strategy, which schedules the computation hardware resource based on the network feature we extract. Evaluation results show our method can increase computational efficiency and lead to a higher resource utilization.