دورية أكاديمية
Hardware implementation of memristor-based artificial neural networks.
العنوان: | Hardware implementation of memristor-based artificial neural networks. |
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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).) |
References: | Sci Rep. 2015 May 28;5:10492. (PMID: 26020412) Nat Nanotechnol. 2021 Jun;16(6):680-687. (PMID: 33737724) Nature. 2020 Jan;577(7792):641-646. (PMID: 31996818) IEEE Trans Neural Netw Learn Syst. 2018 Oct;29(10):4782-4790. (PMID: 29990267) ACS Appl Mater Interfaces. 2020 Jan 8;12(1):1069-1077. (PMID: 31820625) Nat Commun. 2023 Aug 30;14(1):5282. (PMID: 37648721) Nat Commun. 2018 Nov 30;9(1):5106. (PMID: 30504804) Nat Nanotechnol. 2020 Jul;15(7):529-544. (PMID: 32231270) Sci Rep. 2020 Apr 3;10(1):5838. (PMID: 32246103) ACS Nano. 2021 Jan 26;15(1):1764-1774. (PMID: 33443417) Adv Mater. 2021 May;33(21):e2008709. (PMID: 33860581) Micromachines (Basel). 2019 May 07;10(5):. (PMID: 31067708) IEEE Trans Neural Netw. 1991;2(2):205-13. (PMID: 18276373) ACS Appl Mater Interfaces. 2019 Dec 26;11(51):48029-48038. (PMID: 31789034) Nat Commun. 2018 Dec 10;9(1):5267. (PMID: 30531798) Science. 1983 May 13;220(4598):671-80. (PMID: 17813860) Nature. 2022 Aug;608(7923):504-512. (PMID: 35978128) Front Neurosci. 2020 Jan 09;13:1383. (PMID: 31998059) Sensors (Basel). 2020 Dec 30;21(1):. (PMID: 33396796) Nanotechnology. 2019 Jul 25;30(44):445205. (PMID: 31341103) Nat Commun. 2017 May 12;8:15199. (PMID: 28497781) Nature. 2017 Jul 5;547(7661):74-78. (PMID: 28682331) Sensors (Basel). 2022 May 19;22(10):. (PMID: 35632270) IEEE Trans Neural Netw Learn Syst. 2014 Oct;25(10):1864-78. (PMID: 25291739) Nat Commun. 2013;4:2072. (PMID: 23797631) Front Neurosci. 2020 May 12;14:406. (PMID: 32477047) Nat Commun. 2018 Dec 14;9(1):5311. (PMID: 30552327) IEEE Trans Neural Netw. 1997;8(3):532-48. (PMID: 18255657) Phys Chem Chem Phys. 2016 Apr 14;18(14):9338-43. (PMID: 26996120) Nanotechnology. 2019 Aug 30;30(35):352003. (PMID: 31071689) Front Neurosci. 2016 Jul 21;10:333. (PMID: 27493624) Nature. 2019 Aug;572(7767):106-111. (PMID: 31367028) Nature. 2015 May 7;521(7550):61-4. (PMID: 25951284) Front Neurosci. 2019 Jun 12;13:593. (PMID: 31249502) Sci Rep. 2017 Dec 13;7(1):17532. (PMID: 29235524) Nature. 2018 Jun;558(7708):60-67. (PMID: 29875487) Nat Commun. 2021 Mar 31;12(1):1984. (PMID: 33790277) Sci Rep. 2015 May 05;5:10123. (PMID: 25941950) Front Neurosci. 2020 Mar 24;14:240. (PMID: 32265641) Nanoscale. 2016 Aug 7;8(29):14015-22. (PMID: 27143476) Micromachines (Basel). 2020 Apr 18;11(4):. (PMID: 32325690) ACS Appl Mater Interfaces. 2017 Nov 22;9(46):40420-40427. (PMID: 29086551) Neural Netw. 2018 Dec;108:217-223. (PMID: 30216871) Nanoscale. 2017 Jul 13;9(27):9275-9283. (PMID: 28657078) Nat Commun. 2021 Aug 31;12(1):5198. (PMID: 34465783) Science. 2014 Aug 8;345(6197):668-73. (PMID: 25104385) Adv Mater. 2009 Jul 13;21(25-26):2632-2663. (PMID: 36751064) Nat Commun. 2018 Jun 19;9(1):2385. (PMID: 29921923) Nature. 2008 May 1;453(7191):80-3. (PMID: 18451858) Nat Commun. 2017 Jun 05;8:15666. (PMID: 28580928) Adv Mater. 2018 Mar;30(9):. (PMID: 29318659) Front Neuroinform. 2018 Dec 12;12:89. (PMID: 30631269) Sci Adv. 2021 Nov 26;7(48):eabj4801. (PMID: 34818038) Elife. 2019 Aug 20;8:. (PMID: 31429824) Nature. 2022 Apr;604(7905):255-260. (PMID: 35418630) Nat Commun. 2020 May 18;11(1):2473. (PMID: 32424184) |
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 |
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