  Biologically plausible Spiking Neural Networks (SNNs), characterized by spike
sparsity, are growing tremendous attention over intellectual edge devices and
critical bio-medical applications as compared to artificial neural networks
(ANNs). However, there is a considerable risk from malicious attempts to
extract white-box information (i.e., weights) from SNNs, as attackers could
exploit well-trained SNNs for profit and white-box adversarial concerns. There
is a dire need for intellectual property (IP) protective measures. In this
paper, we present a novel secure software-hardware co-designed RRAM-based
neuromorphic accelerator for protecting the IP of SNNs. Software-wise, we
design a tailored genetic algorithm with classic XOR encryption to target the
least number of weights that need encryption. From a hardware perspective, we
develop a low-energy decryption module, meticulously designed to provide zero
decryption latency. Extensive results from various datasets, including NMNIST,
DVSGesture, EEGMMIDB, Braille Letter, and SHD, demonstrate that our proposed
method effectively secures SNNs by encrypting a minimal fraction of stealthy
weights, only 0.00005% to 0.016% weight bits. Additionally, it achieves a
substantial reduction in energy consumption, ranging from x59 to x6780, and
significantly lowers decryption latency, ranging from x175 to x4250. Moreover,
our method requires as little as one sample per class in dataset for encryption
and addresses hessian/gradient-based search insensitive problems. This strategy
offers a highly efficient and flexible solution for securing SNNs in diverse
applications.
