An ultra low power analogue radial basis function Network

This paper provides the circuits which are needed to implement RBF Neural Networks. A novel circuit to implement an M-dimensional Euclidean distance is presented. All the blocks were designed in weak inversion region to gain ultra low power consumption. The results were done using HSPICE by level 49 parameters (BSIM3v3) in 0.35μm standard CMOS technology. Finally, an RBF neuron with five hidden layers to approximate a nonlinear function is implemented. Having confirmed that the proposed circuit is working properly, simulation results have shown that the power consumption of our circuits is about several microwatts.