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Committee Machines—A Universal Method to Deal with Non-Idealities in Memristor-Based Neural Networks

Joksas, D, Freitas, P, Chai, Z, Ng, WH, Buckwell, M, Li, C, Zhang, WD, Xia, QF, Kenyon, AJ and Mehonic, A Committee Machines—A Universal Method to Deal with Non-Idealities in Memristor-Based Neural Networks. Nature Communications. ISSN 2041-1723 (Accepted)

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Abstract

Arti ficial neural networks are notoriously power- and time-consuming when implemented on conventional von Neumann computing systems. Consequently, recent years have seen an emergence of research in machine learning hardware that strives to bring memory and computing closer together. A popular approach is to realise artifi cial neural networks in hardware by implementing their synaptic weights using memristive devices. However, various device- and system-level non-idealities usually prevent these physical implementations from achieving high inference accuracy. We suggest applying a well-known concept in computer science|committee machines|in the context of memristor-based neural networks. Using simulations and experimental data from three different types of memristive devices, we show that committee machines employing ensemble averaging can successfully increase inference accuracy in physically implemented neural networks that suffer from faulty devices, device-to-device variability, random telegraph noise and line resistance. Importantly, we demonstrate that the accuracy can be improved even without increasing the total number of memristors.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Divisions: Electronics & Electrical Engineering (merged with Engineering 10 Aug 20)
Publisher: Nature Research (part of Springer Nature)
Date Deposited: 30 Jul 2020 08:55
Last Modified: 30 Jul 2020 09:00
URI: https://researchonline.ljmu.ac.uk/id/eprint/13408

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