The multilevel resistive random access memory (RRAM)-based synaptic array can enable parallel computations of vector-matrix multiplication for machine learning inference acceleration; however, any conductance drift of the cell may induce an inference accuracy drop because the analog current is summed up along the column. In this article, the read disturb-induced conductance drift characteristic is statistically measured on a test vehicle based on 2-bit HfO2 RRAM array. The drift behavior of four states is empirically modeled by a vertical and lateral filament growth mechanism. Furthermore, a bipolar read scheme is proposed and tested to enhance the resilience against the read disturb. The modeled read disturb and proposed compensation scheme are incorporated into a VGG-like convolutional neural network for CIFAR-10 data set inference.
- In-memory computing
- Multilevel resistive random access memory (RRAM)
- Neural network
- Read disturb
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Electrical and Electronic Engineering