Challenges in materials and devices for Resistive-Switching-based Neuromorphic Computing
Abstract
This tutorial describes challenges and possible avenues for the implementation of the components of a solid-state system, which emulates a biological brain. The tutorial is devoted mostly to a charge-based (i.e. electric controlled) implementation using transition metal oxides materials, which exhibit unique properties that emulate key functionalities needed for this application. In the Introduction, we compare the main differences between a conventional computational machine, based on the Turing-von Neumann paradigm, to a Neuromorphic machine, which tries to emulate important functionalities of a biological brain. We also describe the main electrical properties of biological systems, which would be useful to implement in a charge-based system. In Chapter II, we describe the main components of a possible solid-state implementation. In Chapter III, we describe a variety of Resistive Switching phenomena, which may serve as the functional basis for the implementation of key devices for Neuromorphic computing. In Chapter IV we describe why transition metal oxides, are promising materials for future Neuromorphic machines. Theoretical models describing different resistive switching mechanisms are discussed in Chapter V while existing implementations are described in Chapter VI. Chapter VII presents applications to practical problems. We list in Chapter VIII important basic research challenges and open issues. Wemore »
- Authors:
-
- Univ. of California, San Diego, CA (United States)
- Univ. of Los Andes, Bogotá (Colombia)
- Univ. Paris-Sud, Orsay (France)
- Publication Date:
- Research Org.:
- Energy Frontier Research Centers (EFRC) (United States). Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C); Univ. of California, San Diego, CA (United States); Univ. Paris-Sud, Orsay (France); Univ. of Los Andes, Bogotá (Colombia)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); Office of Naval Research (ONR) (United States); Univ. of Los Andes (Colombia)
- OSTI Identifier:
- 1481895
- Grant/Contract Number:
- SC0019273; N00014-15-1-2848; 120471250659; 120424054303
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Applied Physics
- Additional Journal Information:
- Journal Volume: 124; Journal Issue: 21; Journal ID: ISSN 0021-8979
- Publisher:
- American Institute of Physics (AIP)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY; 97 MATHEMATICS AND COMPUTING; 36 MATERIALS SCIENCE
Citation Formats
del Valle, Javier, Ramirez, Juan Gabriel, Rozenberg, Marcelo J., and Schuller, Ivan K. Challenges in materials and devices for Resistive-Switching-based Neuromorphic Computing. United States: N. p., 2018.
Web. doi:10.1063/1.5047800.
del Valle, Javier, Ramirez, Juan Gabriel, Rozenberg, Marcelo J., & Schuller, Ivan K. Challenges in materials and devices for Resistive-Switching-based Neuromorphic Computing. United States. https://doi.org/10.1063/1.5047800
del Valle, Javier, Ramirez, Juan Gabriel, Rozenberg, Marcelo J., and Schuller, Ivan K. Tue .
"Challenges in materials and devices for Resistive-Switching-based Neuromorphic Computing". United States. https://doi.org/10.1063/1.5047800. https://www.osti.gov/servlets/purl/1481895.
@article{osti_1481895,
title = {Challenges in materials and devices for Resistive-Switching-based Neuromorphic Computing},
author = {del Valle, Javier and Ramirez, Juan Gabriel and Rozenberg, Marcelo J. and Schuller, Ivan K.},
abstractNote = {This tutorial describes challenges and possible avenues for the implementation of the components of a solid-state system, which emulates a biological brain. The tutorial is devoted mostly to a charge-based (i.e. electric controlled) implementation using transition metal oxides materials, which exhibit unique properties that emulate key functionalities needed for this application. In the Introduction, we compare the main differences between a conventional computational machine, based on the Turing-von Neumann paradigm, to a Neuromorphic machine, which tries to emulate important functionalities of a biological brain. We also describe the main electrical properties of biological systems, which would be useful to implement in a charge-based system. In Chapter II, we describe the main components of a possible solid-state implementation. In Chapter III, we describe a variety of Resistive Switching phenomena, which may serve as the functional basis for the implementation of key devices for Neuromorphic computing. In Chapter IV we describe why transition metal oxides, are promising materials for future Neuromorphic machines. Theoretical models describing different resistive switching mechanisms are discussed in Chapter V while existing implementations are described in Chapter VI. Chapter VII presents applications to practical problems. We list in Chapter VIII important basic research challenges and open issues. We discuss issues related to specific implementations, novel materials, devices and phenomena. The development of reliable, fault tolerant, energy efficient devices, their scaling and integration into a Neuromorphic computer may bring us closer to the development of a machine that rivals the brain.},
doi = {10.1063/1.5047800},
journal = {Journal of Applied Physics},
number = 21,
volume = 124,
place = {United States},
year = {Tue Dec 04 00:00:00 EST 2018},
month = {Tue Dec 04 00:00:00 EST 2018}
}
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