Generalized Bell-Shaped Membership Function Generation Circuit for Memristive Neural Networks

A generalized bell-shaped function is an essential building block for a neuro-fuzzy systems and radial basis function neural networks. With the advent of edge computing, analog neuro-chips can potentially speed-up the near sensor computing. In this paper, we present a generalized bell function generator circuit for machine learning architectures. The circuit design of generalized bell-shaped function as the hybrid CMOS-Memristor that is compatible with typical memristive crossbar architecture is presented, where it uses three memristors to control the output current shape. Designed circuit occupies 10 μm² and consumes less than 4.1 μW. The proposed circuit could be used as a standalone neuron for radial basis function neural network, as demonstrated in this work.