Convolutional neural network (CNN) is widely used in many areas such as image recognition, object detection, and self-driving cars and it requires a huge amount of computation and memory usage when the number of layers increases. Hence, it is critical to reduce its computational complexity and memory usage. In this paper, author uses 8-bit fixed-point quantization to greatly reduce the memory space requirement of the feature maps and weights and the accuracy of LeNet-5 with MNIST dataset is only slightly reduced. In the hardware accelerator, author proposes a highly flexible CNN accelerator with reconfigurable layers. The layers contain padding, convolution, ReLU, max-pooling and flatten operations, and they are reconfigurable. The advantage of the proposed method is that by reusing layers or circuits, it is possible to reduce hardware resources.