Hierarchical model-based approach for concurrent testing of neuromorphic architecture

Abhishek Kumar Mishra

Abstract: Neuromorphic architectures that implement spiking neural networks provide a biologically inspired and energy-efficient approach to processing information. These systems use spike trains, where the timing and frequency of spikes drive computation, offering unique advantages in dynamic and event-driven tasks. This paper develops a concurrent testing methodology for neuromorphic architectures, emphasizing Error Detection and Isolation (EDI) through a hierarchical model-based redundancy framework. Our approach uses a software-based monitoring system that compares the discrepancies between the observed and predicted behavior of hardware-mapped neurons at both the system and the neuron levels. We identify key statistical properties of spike trains that are critical for error detection and develop computationally efficient machine learning models to forecast these properties. By combining real-time observations with predictions of neuron behavior, our EDI methodology ensures robust fault detection and isolation. Experimental evaluations using an open source neuromorphic processor design executing benchmark datasets, MNIST, FashionMNIST, and SVHN, demonstrate the effectiveness. We observe high fault coverage with reduced computational overhead, making the EDI scheme suitable for real-time use in neuromorphic systems.