Hire-snn: Harnessing the inherent robustness of energy-efficient deep spiking neural networks by training with crafted input noise
Low-latency deep spiking neural networks (SNNs) have become a promising alternative to
conventional artificial neural networks (ANNs) because of their potential for increased …
conventional artificial neural networks (ANNs) because of their potential for increased …
Snn-rat: Robustness-enhanced spiking neural network through regularized adversarial training
Spiking neural networks (SNNs) are promising to be widely deployed in real-time and safety-
critical applications with the advance of neuromorphic computing. Recent work has …
critical applications with the advance of neuromorphic computing. Recent work has …
Rate gradient approximation attack threats deep spiking neural networks
Abstract Spiking Neural Networks (SNNs) have attracted significant attention due to their
energy-efficient properties and potential application on neuromorphic hardware. State-of-the …
energy-efficient properties and potential application on neuromorphic hardware. State-of-the …
A comprehensive review of spiking neural networks: Interpretation, optimization, efficiency, and best practices
K Malcolm, J Casco-Rodriguez - arXiv preprint arXiv:2303.10780, 2023 - arxiv.org
Biological neural networks continue to inspire breakthroughs in neural network
performance. And yet, one key area of neural computation that has been under-appreciated …
performance. And yet, one key area of neural computation that has been under-appreciated …
Towards energy-efficient and secure edge AI: A cross-layer framework ICCAD special session paper
The security and privacy concerns along with the amount of data that is required to be
processed on regular basis has pushed processing to the edge of the computing systems …
processed on regular basis has pushed processing to the edge of the computing systems …
Trustworthy Artificial Intelligence Methods for Users' Physical and Environmental Security: A Comprehensive Review
S Szymoniak, F Depta, Ł Karbowiak, M Kubanek - Applied Sciences, 2023 - mdpi.com
Artificial Intelligence is an indispensable element of the modern world, constantly evolving
and contributing to the emergence of new technologies. We meet it in everyday applications …
and contributing to the emergence of new technologies. We meet it in everyday applications …
Enhancing the robustness of spiking neural networks with stochastic gating mechanisms
Spiking neural networks (SNNs) exploit neural spikes to provide solutions for low-power
intelligent applications on neuromorphic hardware. Although SNNs have high computational …
intelligent applications on neuromorphic hardware. Although SNNs have high computational …
Dvs-attacks: Adversarial attacks on dynamic vision sensors for spiking neural networks
A Marchisio, G Pira, M Martina… - … Joint Conference on …, 2021 - ieeexplore.ieee.org
Spiking Neural Networks (SNNs), despite being energy-efficient when implemented on
neuromorphic hardware and coupled with event-based Dynamic Vision Sensors (DVS), are …
neuromorphic hardware and coupled with event-based Dynamic Vision Sensors (DVS), are …
Toward robust spiking neural network against adversarial perturbation
As spiking neural networks (SNNs) are deployed increasingly in real-world efficiency critical
applications, the security concerns in SNNs attract more attention. Currently, researchers …
applications, the security concerns in SNNs attract more attention. Currently, researchers …
Threaten spiking neural networks through combining rate and temporal information
Spiking Neural Networks (SNNs) have received widespread attention in academic
communities due to their superior spatio-temporal processing capabilities and energy …
communities due to their superior spatio-temporal processing capabilities and energy …