Efficient identification of critical faults in neuromorphic hardware of a neural network

Abstract

The disclosure provides misclassification-driven training (MDT) that efficiently identifies critical faults in neuromorphic hardware, such as a memristor crossbar. MDT advantageously identifies whether a hardware fault is a critical fault and can be used to limit fault recovery when a hardware fault is not a critical fault. By applying fault-tolerant techniques directed to critical faults, such as only for critical faults, processing overhead of a neural network can be reduced. In one aspect, the disclosure provides a method of identifying critical faults in neuromorphic hardware of a neural network. In one example the method of identifying includes: (1) determining a significant parameter of a trained neural network that impacts classification of a sample of a dataset, (2) obtaining a location of the significant parameter in the neuromorphic hardware, and (3) identifying the location as a critical fault of the neuromorphic hardware.