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ReRAM-based neuromorphic computing


Indiveri, Giacomo; Linn, Eike; Ambrogio, Stefano (2016). ReRAM-based neuromorphic computing. In: Ielmini, Daniele; Waser, Rainer. Resistive switching : from fundamentals of nanoionic redox processes to memristive device applications. Hoboken, New Jersey, United States: Wiley VCH, 715-730.

Abstract

Artificial neural networks are intended to solve complex machine-learning tasks by using massive parallel data processing in a similar way as in biological neural systems. A recent approach to mimic biology is to emulate the basic processing elements directly in hardware. ReRAM devices are considered key elements to realize highly scalable, and low-power neuromorphic systems consist of using a hybrid analog–digital circuit approach. The specific properties of ReRAM devices that make those devices highly useful as artificial synapse are highlighted in detail. Especially, the multilevel capability of ReRAM devices enables the implementation of learning rules such as spike-timing-dependent plasticity (STDP). The scaling perspectives of ReRAM-based neuromorphic architectures are elaborated on, revealing a scaling potential below 10 nm. Finally, several neuromorphic applications using ReRAM architectures are reviewed and an evaluation of the future perspectives is given.

Abstract

Artificial neural networks are intended to solve complex machine-learning tasks by using massive parallel data processing in a similar way as in biological neural systems. A recent approach to mimic biology is to emulate the basic processing elements directly in hardware. ReRAM devices are considered key elements to realize highly scalable, and low-power neuromorphic systems consist of using a hybrid analog–digital circuit approach. The specific properties of ReRAM devices that make those devices highly useful as artificial synapse are highlighted in detail. Especially, the multilevel capability of ReRAM devices enables the implementation of learning rules such as spike-timing-dependent plasticity (STDP). The scaling perspectives of ReRAM-based neuromorphic architectures are elaborated on, revealing a scaling potential below 10 nm. Finally, several neuromorphic applications using ReRAM architectures are reviewed and an evaluation of the future perspectives is given.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2016
Deposited On:26 Jan 2017 13:45
Last Modified:26 Jan 2017 13:51
Publisher:Wiley VCH
ISBN:978-3-527-33417-9
Additional Information:Chapter 25
Publisher DOI:https://doi.org/10.1002/9783527680870.ch25
Related URLs:https://doi.org/10.1002/9783527680870 (Publisher)
http://www.recherche-portal.ch/ZAD:default_scope:ebi01_prod010661841 (Library Catalogue)
http://eu.wiley.com/WileyCDA/WileyTitle/productCd-3527334173.html (Publisher)

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