Respawn: Energy-efficient fault-tolerance for spiking neural networks considering unreliable memories

RVW Putra, MA Hanif… - 2021 IEEE/ACM …, 2021 - ieeexplore.ieee.org
Spiking neural networks (SNNs) have shown a potential for having low energy with
unsupervised learning capabilities due to their biologically-inspired computation. However …
被引用次数：33 相关文章

ReSpawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memories

RV Wicaksana Putra, MA Hanif… - 2021 IEEE/ACM …, 2021 - dl.acm.org
Spiking neural networks (SNNs) have shown a potential for having low energy with
unsupervised learning capabilities due to their biologically-inspired computation. However …
[PDF] researchgate.net

[PDF][PDF] ReSpawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memories

RVW Putra, MA Hanif, M Shafique - researchgate.net
Spiking neural networks (SNNs) have shown a potential for having low energy with
unsupervised learning capabilities due to their biologically-inspired computation. However …

Respawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memories

RVW Putra, MA Hanif… - 40th IEEE/ACM …, 2021 - nchr.elsevierpure.com
Spiking neural networks (SNNs) have shown a potential for having low energy with
unsupervised learning capabilities due to their biologically-inspired computation. However …
[PDF] academia.edu

[PDF][PDF] ReSpawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memories

RVW Putra, MA Hanif, M Shafique - academia.edu
Spiking neural networks (SNNs) have shown a potential for having low energy with
unsupervised learning capabilities due to their biologically-inspired computation. However …

Respawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memories

RVW Putra, MA Hanif… - 40th IEEE/ACM …, 2021 - nyuscholars.nyu.edu
Spiking neural networks (SNNs) have shown a potential for having low energy with
unsupervised learning capabilities due to their biologically-inspired computation. However …

ReSpawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memories

RVW Putra, MA Hanif, M Shafique - arXiv preprint arXiv:2108.10271, 2021 - arxiv.org
Spiking neural networks (SNNs) have shown a potential for having low energy with
unsupervised learning capabilities due to their biologically-inspired computation. However …

ReSpawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memories

RV Wicaksana Putra, MA Hanif… - 2021 IEEE/ACM …, 2021 - repositum.tuwien.at
Spiking neural networks (SNNs) have shown a potential for having low energy with
unsupervised learning capabilities due to their biologically-inspired computation. However …