دورية أكاديمية
أعرض في EDS

Hardware implementation of memristor-based artificial neural networks.

التفاصيل البيبلوغرافية
العنوان: Hardware implementation of memristor-based artificial neural networks.
المؤلفون: Aguirre F; Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.; Departament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona (UAB), 08193, Barcelona, Spain., Sebastian A; IBM Research - Zurich, Rüschlikon, Switzerland., Le Gallo M; IBM Research - Zurich, Rüschlikon, Switzerland., Song W; Department of Electrical and Computer Engineering, University of Southern California (USC), Los Angeles, CA, 90089, USA., Wang T; Department of Electrical and Computer Engineering, University of Southern California (USC), Los Angeles, CA, 90089, USA., Yang JJ; Department of Electrical and Computer Engineering, University of Southern California (USC), Los Angeles, CA, 90089, USA., Lu W; Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA., Chang MF; Department of Electrical Engineering, National Tsing Hua University, Hsinchu, 30013, Taiwan., Ielmini D; Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IUNET, Piazza L. da Vinci 32, 20133, Milano, Italy., Yang Y; School of Electronic and Computer Engineering, Peking University, Shenzhen, China., Mehonic A; Department of Electronic and Electrical Engineering, University College London (UCL), Torrington Place, WC1E 7JE, London, UK., Kenyon A; Department of Electronic and Electrical Engineering, University College London (UCL), Torrington Place, WC1E 7JE, London, UK., Villena MA; Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia., Roldán JB; Departamento de Electrónica y Tecnología de Computadores, Facultad de Ciencias, Universidad de Granada, Avenida Fuentenueva s/n, 18071, Granada, Spain., Wu Y; Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA., Hsu HH; Department of Electrical Engineering, National Tsing Hua University, Hsinchu, 30013, Taiwan., Raghavan N; Engineering Product Development (EPD) Pillar, Singapore University of Technology & Design, 8 Somapah Road, 487372, Singapore, Singapore., Suñé J; Departament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona (UAB), 08193, Barcelona, Spain., Miranda E; Departament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona (UAB), 08193, Barcelona, Spain., Eltawil A; Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia., Setti G; Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia., Smagulova K; Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia., Salama KN; Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia., Krestinskaya O; Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia., Yan X; Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei University, Baoding, 071002, China., Ang KW; Department of Electrical and Computer Engineering, College of Design and Engineering, National University of Singapore (NUS), Singapore, Singapore., Jain S; Department of Electrical and Computer Engineering, College of Design and Engineering, National University of Singapore (NUS), Singapore, Singapore., Li S; Department of Electrical and Computer Engineering, College of Design and Engineering, National University of Singapore (NUS), Singapore, Singapore., Alharbi O; Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia., Pazos S; Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia., Lanza M; Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia. mario.lanza@kaust.edu.sa.
المصدر: Nature communications [Nat Commun] 2024 Mar 04; Vol. 15 (1), pp. 1974. Date of Electronic Publication: 2024 Mar 04.
نوع المنشور: Journal Article; Review
اللغة: English
بيانات الدورية: Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: PubMed not MEDLINE; MEDLINE
أسماء مطبوعة: Original Publication: [London] : Nature Pub. Group
مستخلص: 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.
(© 2024. The Author(s).)
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تواريخ الأحداث: Date Created: 20240304 Latest Revision: 20240307
رمز التحديث: 20240307
مُعرف محوري في PubMed: PMC10912231
DOI: 10.1038/s41467-024-45670-9
PMID: 38438350
قاعدة البيانات: MEDLINE
الوصف
تدمد:2041-1723
DOI:10.1038/s41467-024-45670-9