NVIDIA Corporation
ONLINE FAULT DETECTION IN RERAM-BASED AI/ML

Abstract:

The disclosure describes a method of monitoring the dynamic power consumption of ReRAM crossbars and determines the occurrence of faults when a changepoint is detected in the monitored power-consumption time series. Statistical features are computed 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. With the proposed fault-detection method and the predictive model, the test time is significantly reduced while high classification accuracy for well-known AI/ML datasets using a ReRAM-based computing system (RCS) can still be ensured.