[PDF][PDF] Toward robust spiking neural network against adversarial perturbation

L Liang, K Xu, X Hu, L Deng… - Advances in Neural …, 2022 - proceedings.neurips.cc
As spiking neural networks (SNNs) are deployed increasingly in real-world efficiency critical
applications, the security concerns in SNNs attract more attention. Currently, researchers …

Threaten spiking neural networks through combining rate and temporal information

Z Hao, T Bu, X Shi, Z Huang, Z Yu… - The Twelfth International …, 2023 - openreview.net
Spiking Neural Networks (SNNs) have received widespread attention in academic
communities due to their superior spatio-temporal processing capabilities and energy …

Special session: Towards an agile design methodology for efficient, reliable, and secure ML systems

S Dave, A Marchisio, MA Hanif… - 2022 IEEE 40th VLSI …, 2022 - ieeexplore.ieee.org
The real-world use cases of Machine Learning (ML) have exploded over the past few years.
However, the current computing infrastructure is insufficient to support all real-world …

Enhancing adversarial robustness in SNNs with sparse gradients

Y Liu, T Bu, J Ding, Z Hao, T Huang, Z Yu - arxiv preprint arxiv …, 2024 - arxiv.org
Spiking Neural Networks (SNNs) have attracted great attention for their energy-efficient
operations and biologically inspired structures, offering potential advantages over Artificial …

Robust stable spiking neural networks

J Ding, Z Pan, Y Liu, Z Yu, T Huang - arxiv preprint arxiv:2405.20694, 2024 - arxiv.org
Spiking neural networks (SNNs) are gaining popularity in deep learning due to their low
energy budget on neuromorphic hardware. However, they still face challenges in lacking …