A multilayer feedforward neural network model for digital hardware implementation

A design algorithm for two-layer feedforward neural networks (2FNNs) for discrete input-output mapping is proposed. In this algorithm, uniformly quantized discrete weights are used, which could be in the form of either one-powers-of-two (OPOT) values or sum-of-powers-of-two (SPOT) values. The simplified sigmoid activation functions (SSAFs) are used at hidden neurons and the step functions are used at output neurons to further reduce the hardware implementation cost. Simulation results indicate that such networks can retain nearly identical recall performances as those of the corresponding networks using continuous weights and sigmoid activation functions (SAFs), while having increased computational speed in applications and reduced cost in digital hardware implementation