The Implementation of Activation Function of CNN Based on FPGA Using Single Precision Floating-Point-Unit

Convolutional Neural Network (CNN) was gotten attention because of the presentation of Deep-Learning. It has large research value for Deep-Learning in using FPGAs. The activation function is the most necessary part of CNN. In this paper, Sigmoid function was chosen as the experiment object. The approximation ways of Sigmoid Function were listed and analysed, the piecewise forth-order approximation was the best way to fit Sigmoid Function. Parallel calculation circuit was designed by Verilog HDL on FPGAs, collecting dataset and inputing them to FPGA Platform and CPU Platform. The experiment result set forth that this solution had high efficiency and low error with rosy prospect about Deep-Learning and FPGA.