  Field Programmable Gate Arrays (FPGAs) plays an increasingly important role
in data sampling and processing industries due to its highly parallel
architecture, low power consumption, and flexibility in custom algorithms.
Especially, in the artificial intelligence field, for training and implement
the neural networks and machine learning algorithms, high energy efficiency
hardware implement and massively parallel computing capacity are heavily
demanded. Therefore, many global companies have applied FPGAs into AI and
Machine learning fields such as autonomous driving and Automatic Spoken
Language Recognition (Baidu) [1] [2] and Bing search (Microsoft) [3].
Considering the FPGAs great potential in these fields, we tend to implement a
general neural network hardware architecture on XILINX ZU9CG System On Chip
(SOC) platform [4], which contains abundant hardware resource and powerful
processing capacity. The general neural network architecture on the FPGA SOC
platform can perform forward and backward algorithms in deep neural networks
(DNN) with high performance and easily be adjusted according to the type and
scale of the neural networks.
