On the Asymmetry of Stuck-at Fault Sensitivity in Memristive Neural Architectures
Document Type
Conference Article
Publication Title
2024 IEEE 8th International Test Conference India Itc India 2024
Abstract
The use of memristive crossbar-based architectures has gained traction as a potential solution for performing computationally expensive tasks such as matrix-vector multiplication and vector outer product, which require significant amount of space, time, and energy. Despite being deemed inherently fault-tolerant, memristive crossbar-based neural architecture (MCNA) may often experience accuracy degradation due to hardware faults, resulting in significant variations. This study aims to comprehensively analyze the impact of stuck-at faults (SAFs) on the accuracy of a neural network during classification or regression. Contrary to the popular belief, it is observed that the impact of stuck-at-0 (SAO) and stuck-at-l (SAl) faults are highly asymmetric with respect to the loss of accuracy. Thus this study might help in planning test strategies for the enhancement of fault immunity in memristive neural architectures.
DOI
10.1109/ITCIndia62949.2024.10652097
Publication Date
1-1-2024
Recommended Citation
Mondal, Manobendra Nath; Chowdhury, Animesh Basak; Prajapati, Santlal; Sur-Kolay, Susmita; and Bhattacharya, Bhargab B., "On the Asymmetry of Stuck-at Fault Sensitivity in Memristive Neural Architectures" (2024). Conference Articles. 884.
https://digitalcommons.isical.ac.in/conf-articles/884