Emerging Memory Devices for Neuromorphic Computing

• Published in: Advanced materials technologies 2019-04, Vol.4 (4), p.n/a
• Main Authors: Upadhyay, Navnidhi K., Jiang, Hao, Wang, Zhongrui, Asapu, Shiva, Xia, Qiangfei, Joshua Yang, J.
• Format: Article
• Language: English
• Subjects: artificial synapses; emerging memory technologies; memristors; neuromorphic systems; synaptic transistors
• ISSN: 2365-709X; 2365-709X
• DOI: 10.1002/admt.201800589

A neuromorphic computing system may be able to learn and perform a task on its own by interacting with its surroundings. Combining such a chip with complementary metal–oxide–semiconductor (CMOS)‐based processors can potentially solve a variety of problems being faced by today's artificial intelligence (AI) systems. Although various architectures purely based on CMOS are designed to maximize the computing efficiency of AI‐based applications, the most fundamental operations including matrix multiplication and convolution heavily rely on the CMOS‐based multiply–accumulate units which are ultimately limited by the von Neumann bottleneck. Fortunately, many emerging memory devices can naturally perform vector matrix multiplication directly utilizing Ohm's law and Kirchhoff's law when an array of such devices is employed in a cross‐bar architecture. With certain dynamics, these devices can also be used either as synapses or neurons in a neuromorphic computing system. This paper discusses various emerging nanoscale electronic devices that can potentially reshape the computing paradigm in the near future. Neuromorphic computing system takes its inspiration from the brain and it outperforms conventional computers (Von Neumann) in terms of energy consumption, reconfigurability, fault tolerance and scalability in many tasks that need human like thinking and learning. This article presents a timely review of various emerging nanoscale electronic devices that could potentially be used to realize such system on a hardware.