eprintid: 10188620 rev_number: 7 eprint_status: archive userid: 699 dir: disk0/10/18/86/20 datestamp: 2024-03-07 11:57:41 lastmod: 2024-03-07 11:57:41 status_changed: 2024-03-07 11:57:41 type: article metadata_visibility: show sword_depositor: 699 creators_name: Aguirre, Fernando creators_name: Sebastian, Abu creators_name: Le Gallo, Manuel creators_name: Song, Wenhao creators_name: Wang, Tong creators_name: Yang, J Joshua creators_name: Lu, Wei creators_name: Chang, Meng-Fan creators_name: Ielmini, Daniele creators_name: Yang, Yuchao creators_name: Mehonic, Adnan creators_name: Kenyon, Anthony creators_name: Villena, Marco A creators_name: Roldán, Juan B creators_name: Wu, Yuting creators_name: Hsu, Hung-Hsi creators_name: Raghavan, Nagarajan creators_name: Suñé, Jordi creators_name: Miranda, Enrique creators_name: Eltawil, Ahmed creators_name: Setti, Gianluca creators_name: Smagulova, Kamilya creators_name: Salama, Khaled N creators_name: Krestinskaya, Olga creators_name: Yan, Xiaobing creators_name: Ang, Kah-Wee creators_name: Jain, Samarth creators_name: Li, Sifan creators_name: Alharbi, Osamah creators_name: Pazos, Sebastian creators_name: Lanza, Mario title: Hardware implementation of memristor-based artificial neural networks ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F46 keywords: Electrical and electronic engineering, Electronic devices note: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. abstract: Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach. date: 2024-03-04 date_type: published publisher: Springer Science and Business Media LLC official_url: https://doi.org/10.1038/s41467-024-45670-9 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2254479 doi: 10.1038/s41467-024-45670-9 pii: 10.1038/s41467-024-45670-9 lyricists_name: Mehonic, Adnan lyricists_name: Kenyon, Anthony lyricists_id: AMEHO63 lyricists_id: AJKEN86 actors_name: Flynn, Bernadette actors_id: BFFLY94 actors_role: owner full_text_status: public publication: Nature Communications volume: 15 article_number: 1974 event_location: England issn: 2041-1723 citation: Aguirre, Fernando; Sebastian, Abu; Le Gallo, Manuel; Song, Wenhao; Wang, Tong; Yang, J Joshua; Lu, Wei; ... Lanza, Mario; + view all <#> Aguirre, Fernando; Sebastian, Abu; Le Gallo, Manuel; Song, Wenhao; Wang, Tong; Yang, J Joshua; Lu, Wei; Chang, Meng-Fan; Ielmini, Daniele; Yang, Yuchao; Mehonic, Adnan; Kenyon, Anthony; Villena, Marco A; Roldán, Juan B; Wu, Yuting; Hsu, Hung-Hsi; Raghavan, Nagarajan; Suñé, Jordi; Miranda, Enrique; Eltawil, Ahmed; Setti, Gianluca; Smagulova, Kamilya; Salama, Khaled N; Krestinskaya, Olga; Yan, Xiaobing; Ang, Kah-Wee; Jain, Samarth; Li, Sifan; Alharbi, Osamah; Pazos, Sebastian; Lanza, Mario; - view fewer <#> (2024) Hardware implementation of memristor-based artificial neural networks. Nature Communications , 15 , Article 1974. 10.1038/s41467-024-45670-9 <https://doi.org/10.1038/s41467-024-45670-9>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10188620/1/s41467-024-45670-9.pdf