Efficient FPGA implementation of a generic function approximator and its application to neural net computation

Typically, digital sigmoid implementations for neural nets have low accuracy or unwieldy memory requirements. The authors presented a highly accurate, memory-efficient sigmoid calculator, designed using a genetic algorithm. The VHDL design, implemented in an Altera Flex10K device, is easily reconfigurable for any sigmoid slope or for computing other required system-on-a-chip functions.