Analog sorting circuit for the application in self-organizing neural networks based on neural gas learning algorithm

The paper presents a new, mixed analog-digital, circuit for analog sorting signals. In comparison to other circuits of this type the proposed solution offers large versatility. The main objective is its application in Neural Gas (NG) learning algorithm used to train unsupervised neural networks (NNs). However, the circuit can also be used in nonlinear processing of analog signals. It is capable of performing simultaneously several typical nonlinear operations that include Min, Max and Median filtering. The circuit offers high accuracy, however the difference between signals that can be distinguished depends on the steepness of a reference ramp signal. For example, the circuit it able to distinguish signals that differ by 10 nA if the assumed time is larger than 1 μs. Since a typical number of neurons in the NN exceeds 100-200, the circuit has been designed to sort so many input signals. The sorting operation provides us values of particular output signals, as well as the information which inputs signals deliver particular output signals. This second feature is used in case of the application of the circuit in NN. The system was implemented in the TSMC 180nm CMOS technology and verified in the HSpice environment. For 8 inputs varying in between 1 to 10 μA the circuit dissipates an average power of 250 μW.