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

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 $\times59$ to $\times6780$, and significantly lowers decryption latency, ranging from $\times175$ to $\times4250$. 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

Encryption Simulation Result

This code performs efficient SNNGX genetic weight-bit (MSB) encryption for low overhead on neuromorphic accelerators. The SNNGX method universally applies to differently pretrained SNN models and overcomes gradient insensitivity problems.

Quick Start

pytorch>=1.10.0+cu111
tonic>=1.4.3

git clone https://github.com/u3556440/SNNGX_qSNN_encryption.git

Usage 🚀🚀

The following datasets are not included in this repository.
Please indicate your path to the Dataset (NMNIST/DVS128_Gesture) in the below __terminal__.sh file.

1. SNNGX Encryption (Genetic Algorithm):

cd Encryption_SNNGX

Example code:

bash __terminal__.sh

Full python argument:

python3 __main__.py [--epsil] [--by_layer] [--layer_idx] [--qbit] [--subset] [-b] [--mutate]  [--gen] [--Dataset] [--Dpath] [--seed]

Flag	VarType	Help
--epsil	int	Final number of bits to be converged by GA
--by_layer	bool	Boolean Flag (True for Layer-wise Encryption / False for All layers Encryption)
--layer_idx	int	Layer idx (starting from 0) for Layer-wise Encryption
-q/--qbit	int	Quantized bit width of SNN (default: 8bit)
--subset	int	Total No. of Samples for encryption dataset
-b/--batch	int	Batch size for dataset, model, encryption dataset
--mutate	float	pixel-wise mutation probability (default: 0.05)
--gen	int	No. of GA_generations
--Dataset	str	Available Dataset: "NMNIST"/"DVS128_Gesture"
--Dpath	str	Please input your local path to the dataset
--seed	int	Random Seed for Repeatable Experimental Result

2. Random bit Encryption:

cd Encryption_Random_bit

Example code:

bash __terminal__.sh

Full python argument:

python3 __main__.py [-nb] [-nt] [--by_layer] [--layer_idx] [--qbit] [-b] [--Dataset] [--Dpath] [--seed]

Flag	VarType	Help
-nb/--numbit	int	Random BITS flipped %
-nt/--numtrial	int	No. of trial repetition
--by_layer	bool	Boolean Flag (True for Layer-wise Encryption / False for All layers Encryption)
--layer_idx	int	Layer idx (starting from 0) for Layer-wise Encryption
-q/--qbit	int	Quantized bit width of SNN (default: 8bit)
-b/--batch	int	Batch size for dataset and model
--Dataset	str	Available Dataset: "NMNIST"/"DVS128_Gesture"
--Dpath	str	Please input your local path to the dataset
--seed	int	Random Seed for Repeatable Experimental Result

Citation

If you find SNNGX code useful and helpful to your work, please kindly cite the paper 🔥 🔥

@article{wong2024snngx,
  title={SNNGX: Securing Spiking Neural Networks with Genetic XOR Encryption on RRAM-based Neuromorphic Accelerator},
  author={Wong, Kwunhang and Wang, Songqi and Huang, Wei and Zhang, Xinyuan and He, Yangu and Lai, Karl MH and Jiao, Yuzhong and Lin, Ning and Qi, Xiaojuan and Chen, Xiaoming and others},
  journal={arXiv preprint arXiv:2407.15152},
  year={2024}
}