Computation of a nonlinear squashing function in digital neural networks

The paper deals with implementing nonlinear sigmoid-like squashing functions in FPGA based neural networks. Five piecewise linear (PWL) approximation techniques and a look-up table are compared. It is examined their performance in terms of recall delay, area requirements, and accuracy in two FPGA neural networks.