Joksas, Dovydas;
AlMutairi, AbdulAziz;
Lee, Oscar;
Cubukcu, Murat;
Lombardo, Antonio;
Kurebayashi, Hidekazu;
Kenyon, Anthony J;
(2022)
Memristive, Spintronic, and 2D‐Materials‐Based Devices to Improve and Complement Computing Hardware.
Advanced Intelligent Systems
, Article 2200068. 10.1002/aisy.202200068.
(In press).
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Advanced Intelligent Systems - 2022 - Joksas - Memristive Spintronic and 2D%E2%80%90Materials%E2%80%90Based Devices to Improve and.pdf - Published Version Download (2MB) | Preview |
Abstract
In a data-driven economy, virtually all industries benefit from advances in information technology -- powerful computing systems are critically important for rapid technological progress. However, this progress might be at risk of slowing down if we do not address the discrepancy between our current computing power demands and what the existing technologies can offer. Key limitations to improving energy efficiency are the excessive growth of data transfer costs associated with the von Neumann architecture and the fundamental limits of complementary metal-oxide-semiconductor (CMOS) technologies, such as transistors. In this perspective article, we discuss three technologies that will likely play an essential role in future computing systems: memristive electronics, spintronics, and electronics based on 2D materials. We present how these may transform conventional digital computers and contribute to the adoption of new paradigms, like neuromorphic computing.
| Type: | Article |
|---|---|
| Title: | Memristive, Spintronic, and 2D‐Materials‐Based Devices to Improve and Complement Computing Hardware |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/aisy.202200068 |
| Publisher version: | https://doi.org/10.1002/aisy.202200068 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Machine learning, memristors, spintronics, neuromorphic computing, 2D materials |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > London Centre for Nanotechnology UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10151153 |
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