Online Fault Detection in ReRAM-Based Computing Systems by Monitoring Dynamic Power Consumption

A ReRAM-based computing system (RCS) provides an energy-efficient hardware implementation of vector-matrix multiplication for machine-learning hardware. However, it is vulnerable to faults due to the immature ReRAM fabrication process. We propose an efficient online fault-detection method for RCS; the proposed method monitors the dynamic power consumption of each ReRAM crossbar and determines the occurrence of faults when a changepoint is detected in the monitored power-consumption time series. In order to estimate the percentage of faulty cells in a faulty ReRAM crossbar, we compute statistical features before and after the changepoint and train a predictive model using machine-learning techniques. In this way, the computationally expensive fault localization and error-recovery steps are carried out only when a high fault rate is estimated. Simulation results show that, with the fault-detection method and the predictive model, the test time is significantly reduced while high classification accuracy for the MNIST and CIFAR-10 datasets using RCS can still be ensured.