Bala, Anu ORCID: https://orcid.org/0009-0000-6242-5248, Khandelwal, Saurabh, Jabir, Abusaleh and Ottavi, Marco (2022) Yield Evaluation of Faulty Memristive Crossbar Array-based Neural Networks with Repairability. In: 2022 IEEE 28th International Symposium on On-Line Testing and Robust System Design (IOLTS). IEEE

Full text not available from this repository.

Abstract

This paper evaluates the yield of a memristor-based crossbar array of artificial neural networks in the presence of stuck-at-faults (SAFs). A technique based on Markov chains is used to estimate the yield in the presence of stuck-at-faults. This method provides a high degree of accuracy. Another method that is used for analysis and comparison is the Poisson distribution, which uses the sum of all repairable fault patterns. A fault repair mechanism is also considered when evaluating the yield of the memristor crossbar array. The results demonstrate that the yield could be improved with redundancies and a higher repairable stuck-at-fault ratio.

Item Type:	Book Section
Status:	Published
DOI:	10.1109/iolts56730.2022.9897183
Subjects:	T Technology > T Technology (General)
School/Department:	London Campus
URI:	https://ray.yorksj.ac.uk/id/eprint/13535

University Staff: Request a correction | RaY Editors: Update this record