Fault Sensitivity Enhancement for Rram-Based Neuromorphic Computing Systems

The complexity of resistive random-access memory (RRAM) based neuromorphic computing systems (RNCS) and the variety of faults make on-line testing challenging. We propose an Iterative Fast Gradient Sign Method (IFGSM) for enhancing the fault sensitivity of RNCS tests. This is accomplished by exploiting the sensitivity of neural networks to weight perturbations. This method is able to enhance the sensitivity to fault perturbations using tests applied only to the primary I/O. Statistical analysis shows that the tests based on IFGSM are more sensitive to small changes in the RNCS than standard test approach.