Recursive sigmoidal neurons for adaptive accuracy neural network implementations

This paper describes an accuracy programmable sigmoidal neuron design and its hardware implementation. The “recursive neuron” can be adjusted to produce recursively more accurate and smoother piecewise linear approximations to the sigmoidal neural squashing function. This adaptive accuracy neuron, combined with a constructive training algorithm, can be used as the basic component for the implementation of self adaptive neural processing systems able to optimize power consumption and processing speeds when operating in applications with changing performance requirements and varying operational constraints.