Skip to Main content Skip to Navigation
Conference papers

Neuron Fault Tolerance in Spiking Neural Networks

Abstract : The error-resiliency of Artificial Intelligence (AI) hardware accelerators is a major concern, especially when they are deployed in mission-critical and safety-critical applications. In this paper, we propose a neuron fault tolerance strategy for Spiking Neural Networks (SNNs). It is optimized for low area and power overhead by leveraging observations made from a largescale fault injection experiment that pinpoints the critical fault types and locations. We describe the fault modeling approach, the fault injection framework, the results of the fault injection experiment, the fault-tolerance strategy, and the fault-tolerant SNN architecture. The idea is demonstrated on two SNNs that we designed for two SNN-oriented datasets, namely the N-MNIST and IBM's DVS128 gesture datasets.
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03036630
Contributor : Haralampos Stratigopoulos Connect in order to contact the contributor
Submitted on : Wednesday, December 2, 2020 - 5:16:52 PM
Last modification on : Friday, December 3, 2021 - 11:42:48 AM
Long-term archiving on: : Wednesday, March 3, 2021 - 8:03:17 PM

File

1401_pdf_upload.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03036630, version 1

Citation

Theofilos Spyrou, Sarah El-Sayed, Engin Afacan, Luis Camuñas-Mesa, Bernabé Linares-Barranco, et al.. Neuron Fault Tolerance in Spiking Neural Networks. 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE), Feb 2021, Grenoble (virtuel), France. ⟨hal-03036630⟩

Share

Metrics

Record views

310

Files downloads

545