LAUSR

Vector neural network (VNN) is one of the most important methods to process interval data. However, the VNN, which contains a great number of multiply-accumulate (MAC) operations, often adopts pure numerical calculation method, and thus is difficult to be miniaturized for the embedded applications. In this paper, we propose a memristor based vector-type backpropagation (MVTBP) architecture which utilizes memristive arrays to accelerate the MAC operations of interval data. Owing to the unique brain-like synaptic characteristics of memristive devices, e.g., small size, low power consumption, and high integration density, the proposed architecture can be implemented with low area and power consumption cost and easily applied to embedded systems. The simulation results indicate that the proposed architecture has better identification performance and noise tolerance. When the device precision is 6 bits and the error deviation level (EDL) is 20%, the proposed architecture can achieve an identification rate, which is about 92% higher than that for interval-value testing sample and 81% higher than that for scalar-value testing sample.