At present, there are many researches on deep neural network (DNN) applied in life. In the task of object recognition, deep convolutional neural network (CNN) has a good performance, but it relies on GPU to solve a large number of complex operations. Thus the hardware accelerator of DNN is concerned by many people. In order to implement the DNN model on hardware, complex connection relationship and memory usage scheduling are needed. This paper presnets the design of FPGA-based accelerator for DNN. The proposed architecture is implemented on Xilinx Zynq-7020 FPGA. It takes the advantages of low latency and low usage in the task of MNIST digital identification, and keeps the 96 % recognition rate.

In this paper, we propose a novel personalized ranking system for amateur photographs. Although some of the features used in our system are similar to previous work, new features, such as texture, RGB color, portrait (through face detection), and black-and-white, are included for individual preferences. Our goal of automatically ranking photographs is not intended for award-wining professional photographs but for photographs taken by amateurs, especially when individual preference is taken into account.