TY  - JOUR
UR  - https://doi.org/10.1038/s41467-024-45670-9
KW  - Electrical and electronic engineering
KW  -  Electronic devices
TI  - Hardware implementation of memristor-based artificial neural networks
N2  - 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.
PB  - Springer Science and Business Media LLC
A1  - Aguirre, Fernando
A1  - Sebastian, Abu
A1  - Le Gallo, Manuel
A1  - Song, Wenhao
A1  - Wang, Tong
A1  - Yang, J Joshua
A1  - Lu, Wei
A1  - Chang, Meng-Fan
A1  - Ielmini, Daniele
A1  - Yang, Yuchao
A1  - Mehonic, Adnan
A1  - Kenyon, Anthony
A1  - Villena, Marco A
A1  - Roldán, Juan B
A1  - Wu, Yuting
A1  - Hsu, Hung-Hsi
A1  - Raghavan, Nagarajan
A1  - Suñé, Jordi
A1  - Miranda, Enrique
A1  - Eltawil, Ahmed
A1  - Setti, Gianluca
A1  - Smagulova, Kamilya
A1  - Salama, Khaled N
A1  - Krestinskaya, Olga
A1  - Yan, Xiaobing
A1  - Ang, Kah-Wee
A1  - Jain, Samarth
A1  - Li, Sifan
A1  - Alharbi, Osamah
A1  - Pazos, Sebastian
A1  - Lanza, Mario
VL  - 15
Y1  - 2024/03/04/
ID  - discovery10188620
SN  - 2041-1723
N1  - 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/.
JF  - Nature Communications
AV  - public
ER  -