LAUSR

The vector neural network (VNN) based on memristor has tremendous potential in applications such as electronic reconnaissance, medical diagnosis, and speech processing. However, the VNNs that encompass a huge amount of multiply-accumulate (MAC) operations often acquire network weights through massive numerical calculations with high precision, which results in a heavy consumption of energy and computing resource. Nevertheless, the resistance states of existing memristive devices can hardly meet the high-precision regulation requirements that restricts the application of VNN. We propose a binary memristor based vector-type back propagation (BMVTBP) architecture that integrates the advantages of low-precision memristive devices and VNN. The core function of this architecture is to realize the low-precision weights of three states by employing binarized memristive synapses, and further construct the positive and negative synaptic arrays to implement the MAC operations of interval data. Simulations verify the identification performance of the BMVTBP architecture on the emitter library. The ensuing results demonstrate that the identification rates exceed 96% and 87% for the interval-value and scalar-value noisy emitter samples, respectively, with an architecture requirement of only 1920 memristive devices and an energy consumption of about 2.43e-10 J.