  As spiking neural networks (SNNs) gain traction in deploying neuromorphic
computing solutions, protecting their intellectual property (IP) has become
crucial. Without adequate safeguards, proprietary SNN architectures are at risk
of theft, replication, or misuse, which could lead to significant financial
losses for the owners. While IP protection techniques have been extensively
explored for artificial neural networks (ANNs), their applicability and
effectiveness for the unique characteristics of SNNs remain largely unexplored.
In this work, we pioneer an investigation into adapting two prominent
watermarking approaches, namely, fingerprint-based and backdoor-based
mechanisms to secure proprietary SNN architectures. We conduct thorough
experiments to evaluate the impact on fidelity, resilience against overwrite
threats, and resistance to compression attacks when applying these watermarking
techniques to SNNs, drawing comparisons with their ANN counterparts. This study
lays the groundwork for developing neuromorphic-aware IP protection strategies
tailored to the distinctive dynamics of SNNs.
