SNNGX: Securing Spiking Neural Networks with Genetic XOR Encryption on RRAM-based Neuromorphic Accelerator

• URL: http://arxiv.org/abs/2407.15152v2
• Date: Mon, 26 Aug 2024 10:20:30 GMT
• Title: SNNGX: Securing Spiking Neural Networks with Genetic XOR Encryption on RRAM-based Neuromorphic Accelerator
• Authors: Kwunhang Wong, Songqi Wang, Wei Huang, Xinyuan Zhang, Yangu He, Karl M. H. Lai, Yuzhong Jiao, Ning Lin, Xiaojuan Qi, Xiaoming Chen, Zhongrui Wang,
• Abstract summary: Spiking Neural Networks (SNNs), characterized by spike sparsity, are growing tremendous attention over intellectual edge devices and critical bio-medical applications. However, there is a considerable risk from malicious attempts to extract white-box information from SNNs. We present a novel secure software- hardware co-designed RRAM-based neuromorphic accelerator for protecting the IP of SNNs.
• Score: 34.474841993360855
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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