Reliability Analysis of a Spiking Neural Network Hardware Accelerator - Circuits Intégrés Numériques et Analogiques Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

Reliability Analysis of a Spiking Neural Network Hardware Accelerator

Résumé

Despite the parallelism and sparsity in neural network models, their transfer into hardware unavoidably makes them susceptible to hardware-level faults. Hardware-level faults can occur either during manufacturing, such as physical defects and process-induced variations, or in the field due to environmental factors and aging. The performance under fault scenarios needs to be assessed so as to develop cost-effective fault-tolerance schemes. In this work, we assess the resilience characteristics of a hardware accelerator for Spiking Neural Networks (SNNs) designed in VHDL and implemented on an FPGA. The fault injection experiments pinpoint the parts of the design that need to be protected against faults, as well as the parts that are inherently fault-tolerant.
Fichier principal
Vignette du fichier
DATE22a.pdf (426.75 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03501968 , version 1 (24-12-2021)

Identifiants

Citer

Theofilos Spyrou, Sarah A El-Sayed, Engin Afacan, Luis A Camuñas-Mesa, Bernabé Linares-Barranco, et al.. Reliability Analysis of a Spiking Neural Network Hardware Accelerator. Design, Automation and Test in Europe Conference (DATE), Mar 2022, Antwerp, Belgium. pp.370-375, ⟨10.23919/DATE54114.2022.9774711⟩. ⟨hal-03501968⟩
216 Consultations
304 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More