Convolutional neural networks (CNNs) have been recognized as the best algorithm for deep learning, and they are widely used in image recognition, automatic translation and advertising recommendations. Due to the increasing size of the neural network, the number of the neurons and synapses of the network is also enlarged. Therefore, using specific acceleration hardware to mine the parallelism of CNNs becomes a popular choice. For hardware design, the classic tiling dataflow has achieved high performance. However, the utilization of processing elements of the tiling structure is very low. As deep learning applications demand higher hardware performance, accelerators require higher utilization of processing elements. In order to achieve this goal, we can change the scheduling order to improve the performance, and use parallel input feature graphs and output channels to improve computing parallelism. However, as neural network computation's demand on hardware performance increases, the array size of processing elements inevitably becomes larger and larger. When the array size is increased to a certain extent, a single parallel approach makes utilization gradually to decrease. This requires hardware to develop more neural network parallelism, thereby suppressing element idling. At the same time, in order to adapt to different network structures, configurable operation of hardware arrays on the neural network is required. But configurable hardware can greatly increase hardware overhead and data scheduling difficulty. So, we propose a configurable neural network accelerator based on tiling dataflow. In order to reduce hardware complexity, we propose a partial configuration technique, which can not only improve the utilization of processing elements under large array, but also reduce hardware overhead as much as possible. When the array size of processing elements exceeds 512, the utilization can maintain at an average of 82%~90%. And the accelerator performance is almost linearly proportional to the number of processing elements.