A non-linear approximation of the sigmoid function based on FPGA

One of the difficult problems encountered when implementing artificial neural networks based on FPGA is the approximation of the activation function. The sigmoid function is the most widely used and is difficult to approximate. This paper is devoted to show a saving hardware resources and accurate way to compute the sigmoid function based on FPGA by non-linear approximation. This is done by subsection analysis involved a new low-leakage FPGA Look-up Tables (LUTs), introducing a non-linear approximation algorithm in detail, analyzing the approximating accuracy and the FPGA hardware resources, which can achieve some kind of balance between the approximating precision and the limited hardware resources of FPGA, shows improvements over the previous known algorithms. The implementation of sigmoid function and the simulation are completed by the development software of QUARTUS II.