Nonlinear Weight Quantification for Mitigating Read Disturb Effect on Multilevel RRAM-Based Neural Network

RRAM -based array has been utilized to accelerate inference process of neural network. However, the device resistance drift during inference will induce read disturb, which will cause significant accuracy drop and cannot be solved by process optimization. Here, we propose a nonlinear weight quantification method to mitigate read disturb effect on inference accuracy. The simulation results indicate that read disturb is well suppressed compared with traditional linear quantification method.