Memristive, Spintronic, and 2D‐Materials‐Based Devices to Improve and Complement Computing Hardware

• Published in: Advanced intelligent systems Vol. 4; no. 8
• Main Authors: Joksas, Dovydas, AlMutairi, AbdulAziz, Lee, Oscar, Cubukcu, Murat, Lombardo, Antonio, Kurebayashi, Hidekazu, Kenyon, Anthony J., Mehonic, Adnan
• Format: Journal Article
• Language: English
• Published: Weinheim John Wiley & Sons, Inc 01.08.2022; Wiley
• Subjects: 2D materials; Anisotropy; Artificial intelligence; Boolean; CMOS; Data transfer (computers); Deep learning; Digital computers; Electrodes; Electronics; Energy; Internet of Things; machine learning; Magnetic fields; memristors; neuromorphic computing; Principles; Spintronics; Transistors; Two dimensional materials
• ISSN: 2640-4567; 2640-4567
• DOI: 10.1002/aisy.202200068

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 the discrepancy between the current computing power demands and what the existing technologies can offer is not addressed. 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. Herein, three approaches that will likely play an essential role in future computing systems are discussed: memristive electronics, spintronics, and electronics based on 2D materials. The authors present how these technologies may transform conventional digital computers and contribute to the adoption of new paradigms, like neuromorphic computing. Moore's law has slowed down and, with the rise of data‐intensive applications, like machine learning, new approaches to computing hardware are needed. The perspective explores the role of memristive, spintronic, and 2D‐materials‐based devices in making conventional computers faster and more energy efficient, and in adopting new paradigms, like neuromorphic computing.