Spiking Neural Networks (SNNs) have emerged as an energy-efficient alternative to Artificial Neural Networks (ANNs), particularly for edge-computing and safety-critical applications. Unlike conventional ANNs, SNNs leverage sparse event-driven processing to reduce energy consumption while significantly maintaining high computational efficiency. This paper presents a framework designed to optimize the conversion of ANNs into equivalent SNNs while balancing accuracy, reliability, and energy efficiency. The proposed framework systematically explores SNN hyperparameters to identify configurations that achieve superior performance compared to their ANN counterparts. Experimental evaluations on MNIST and Fashion-MNIST datasets with different network topologies demonstrate that the optimized SNNs achieve comparable accuracy while offering in some cases 27.81× and 15.17× lower energy consumption and 1.92× and 1.84× less accuracy drop in the presence of faults, respectively, over the ANN baseline. The results highlight the applicability of SNNs in reliability-critical power-constrained environments.
Sharifian et al. (Tue,) studied this question.