Competing memristors for brain-inspired computing

Seung Ju Kim; Sang Bum Kim; Ho Won Jang

doi:10.1016/j.isci.2020.101889

Competing memristors for brain-inspired computing

iScience. 2020 Dec 3;24(1):101889. doi: 10.1016/j.isci.2020.101889. eCollection 2021 Jan 22.

Authors

Seung Ju Kim¹, Sang Bum Kim¹, Ho Won Jang¹

Affiliation

¹ Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Republic of Korea.

Abstract

The expeditious development of information technology has led to the rise of artificial intelligence (AI). However, conventional computing systems are prone to volatility, high power consumption, and even delay between the processor and memory, which is referred to as the von Neumann bottleneck, in implementing AI. To address these issues, memristor-based neuromorphic computing systems inspired by the human brain have been proposed. A memristor can store numerous values by changing its resistance and emulate artificial synapses in brain-inspired computing. Here, we introduce six types of memristors classified according to their operation mechanisms: ionic migration, phase change, spin, ferroelectricity, intercalation, and ionic gating. We review how memristor-based neuromorphic computing can learn, infer, and even create, using various artificial neural networks. Finally, the challenges and perspectives in the competing memristor technology for neuromorphic computing systems are discussed.

Keywords: Computing Methodology; Devices; Electromagnetics; Magnetism.

Publication types

Review