Resiliency Approaches in Convolutional, Photonic, and Spiking Neural Networks

This study presents a comparative examination of state-of-the-art resiliency approaches of Convolutional, Spiking, and Photonic neural networks (CNNs, SNNs, PNNs), their fault and error models, and the main fault tolerance techniques.

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Bibliographic Details
Published in:	2024 IEEE 25th Latin American Test Symposium (LATS) pp. 1 - 10
Main Authors:	Bosio, A., Gomes, M., Pavanello, F., Porsia, A., Ruospo, A., Sanchez, E., Vatajelu, E. I.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 09-04-2024
Subjects:	AI safety Convolutional neural networks Fault Injection Fault tolerance Fault tolerant systems Functional Safety Neural networks Photonic Neural Networks Photonics Reliability Resilience Safety Spiking Neural Networks
Online Access:	Get full text
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Description
Summary:	This study presents a comparative examination of state-of-the-art resiliency approaches of Convolutional, Spiking, and Photonic neural networks (CNNs, SNNs, PNNs), their fault and error models, and the main fault tolerance techniques.
DOI:	10.1109/LATS62223.2024.10534615