Hierarchical Model-Based Approach for ConcurrentTesting of Neuromorphic Architecture

Neuromorphic architectures, which implement spik-

ing 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 be-

tween 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. Experi-

mental 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.