A Modeling Method of Neural Network Activation Function Based on FPGA Multiprocessor

With the development of artificial intelligence, Neural-Network based algorithms are widely applied in various fields, which causes the network structure more complex and the number of neurons also increases. Activation function is the most important part of the neuron, and has nonlinear characteristics. Therefore, the modeling of activation function on FPGA is faced with the problem of too many iterations and a large amount of hardware resource consumption. In this paper, a modeling method of Sigmoid function based on FPGA multi-processor is proposed. In this method, different processor cores are respectively used to control different modules, such as communication with peripherals, scheduling of computing resources, sharing of hardware resources, etc., so as to optimize the use of hardware resources and accelerate the computing. The result cased with the Sigmoid function shows that the method presented in this paper is feasible. And the comparison and analysis of the resources usages and simulation results are given.