A Hybrid Weight Quantization Strategy for Memristive Neural Networks

Abstract

Due to the ability to store data and process information, the memristor-based neuromorphic system has attracted extensive attention. Its efficient parallel computing approach allows it to implement neural networks in hardware. However, due to the limitation of the range of memristor conductance, it is difficult to represent high-precision weights in memristive neural network. During the off-chip learning, it is crucial to find an efficient weight quantization scheme and map it to the memristor array. Therefore, a hybrid weight quantization strategy that combines uniform and non-uniform quantization is proposed to overcome these problems. Specifically, the curve fitting of pulse modulation for tantalum oxide-based memristor is carried out, and the mapping rules of weights are proposed to simplify the process of reading verification. Furthermore, the hybrid quantization strategy is proposed and applied to a multilayer perceptron and a convolutional neural network, respectively. The effectiveness and robustness of the hybrid quantization scheme are verified in the MNIST dataset. Experiments show that the proposed hybrid quantization scheme can achieve 99.26% accuracy at 4 bits and tolerate 20% noise interference. The simulation results in this paper also provide an effective solution for the hardware implementation of memristive neural networks.