  Spiking Neural Networks (SNNs) have emerged as a promising paradigm, offering
event-driven and energy-efficient computation. In recent studies, various
devices tailored for SNN synapses and neurons have been proposed, leveraging
the unique characteristics of emerging non-volatile memory (eNVM) technologies.
While substantial progress has been made in exploring the capabilities of SNNs
and designing dedicated hardware components, there exists a critical gap in
establishing a unified approach for evaluating hardware-level metrics.
Specifically, metrics such as latency, and energy consumption, are pivotal in
assessing the practical viability and efficiency of the constructed neural
network. In this article, we address this gap by presenting a comprehensive
framework for evaluating hardware-level metrics in SNNs based on non-volatile
memory devices. We systematically analyze the impact of synaptic and neuronal
components on energy consumption providing a unified perspective for assessing
the overall efficiency of the network. In this study, our emphasis lies on the
neuron and synaptic device based on magnetic skyrmions. Nevertheless, our
framework is versatile enough to encompass other emerging devices as well.
Utilizing our proposed skyrmionic devices, the constructed SNN demonstrates an
inference accuracy of approximately 98% and achieves energy consumption on the
order of pJ when processing the Modified National Institute of Standards and
Technology (MNIST) handwritten digit dataset.
