Model-Based Approach Towards Correctness Checking of Neuromorphic Computing Systems

Neuromorphic hardware that emulates the neural structure of the human brain can implement machine learning models in an extremely energy-efficient manner. It is especially suitable for executing spiking neural networks (SNNs) which comprise spiking neurons interconnected via synapses. The underlying computation is based on spike trains in which the location and frequency of spikes that occur within the network guide the execution. This paper develops a fault detection and isolation (FDI) methodology to monitor the correctness of a neuromorphic program’s execution using model-based redundancy in which a software-based monitor compares discrepancies between the behavior of neurons mapped to hardware and that predicted by a corresponding mathematical model. We identify properties of spike trains generated by neurons that can be used for fault detection and build machine learning models to forecast these properties. Predictions from these models, which describe the nominal behavior of neurons, when combined with real-time observations, form the basis for FDI. Experiments using CARLSim, a high-fidelity SNN simulator, show that the proposed approach achieves high fault coverage using models that can operate with low computational overhead in real time.