Hardware implementation of memristor-based artificial neural networks Article Swipe
Fernando Aguirre
,
Abu Sebastian
,
Manuel Le Gallo
,
Wenhao Song
,
Tong Wang
,
J. Joshua Yang
,
Wei Lü
,
Meng‐Fan Chang
,
Daniele Ielmini
,
Yuchao Yang
,
Adnan Mehonić
,
Anthony J. Kenyon
,
Marco A. Villena
,
J.B. Roldán
,
Yuting Wu
,
Hung-Hsi Hsu
,
Nagarajan Raghavan
,
J. Suñé
,
E. Miranda
,
Ahmed M. Eltawil
,
Gianluca Setti
,
Kamilya Smagulova
,
K. Saláma
,
Olga Krestinskaya
,
Xiaobing Yan
,
Kah‐Wee Ang
,
Samarth Jain
,
Sifan Li
,
Osamah Alharbi
,
Sebastián Pazos
,
Mario Lanza
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41467-024-45670-9
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41467-024-45670-9
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.
Related Topics
Concepts
Memristor
Computer science
Artificial neural network
Physical neural network
Artificial intelligence
Computer architecture
Types of artificial neural networks
Time delay neural network
Electrical engineering
Engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41467-024-45670-9
- https://www.nature.com/articles/s41467-024-45670-9.pdf
- OA Status
- gold
- Cited By
- 284
- References
- 274
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392367648
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4392367648Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41467-024-45670-9Digital Object Identifier
- Title
-
Hardware implementation of memristor-based artificial neural networksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-04Full publication date if available
- Authors
-
Fernando Aguirre, Abu Sebastian, Manuel Le Gallo, Wenhao Song, Tong Wang, J. Joshua Yang, Wei Lü, Meng‐Fan Chang, Daniele Ielmini, Yuchao Yang, Adnan Mehonić, Anthony J. Kenyon, Marco A. Villena, J.B. Roldán, Yuting Wu, Hung-Hsi Hsu, Nagarajan Raghavan, J. Suñé, E. Miranda, Ahmed M. Eltawil, Gianluca Setti, Kamilya Smagulova, K. Saláma, Olga Krestinskaya, Xiaobing Yan, Kah‐Wee Ang, Samarth Jain, Sifan Li, Osamah Alharbi, Sebastián Pazos, Mario LanzaList of authors in order
- Landing page
-
https://doi.org/10.1038/s41467-024-45670-9Publisher landing page
- PDF URL
-
https://www.nature.com/articles/s41467-024-45670-9.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.nature.com/articles/s41467-024-45670-9.pdfDirect OA link when available
- Concepts
-
Memristor, Computer science, Artificial neural network, Physical neural network, Artificial intelligence, Computer architecture, Types of artificial neural networks, Time delay neural network, Electrical engineering, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
284Total citation count in OpenAlex
- Citations by year (recent)
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2025: 213, 2024: 71Per-year citation counts (last 5 years)
- References (count)
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274Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.machines | 40 |
| abstract_inverted_index.metrics. | 183 |
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| abstract_inverted_index.shifting | 79 |
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| abstract_inverted_index.properties, | 103 |
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| abstract_inverted_index.massively-parallel | 112 |
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| abstract_inverted_index.metal-oxide-semiconductor | 87 |
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| countries_distinct_count | 9 |
| institutions_distinct_count | 31 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8999999761581421 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.99990773 |
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| citation_normalized_percentile.is_in_top_10_percent | True |