Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Author:

Go Shao-Xiang1,Lim Kian-Guan1,Lee Tae-Hoon23,Loke Desmond K.1ORCID

Affiliation:

1. Department of Science, Mathematics and Technology Singapore University of Technology and Design Singapore 487372 Singapore

2. Department of Engineering University of Cambridge Trumpington Street Cambridge CB2 1PZ UK

3. School of Materials Science and Engineering Kyungpook National University Daegu 41566 Republic of Korea

Abstract

Separate memory and processing units are utilized in conventional von Neumann computational architectures. However, regarding the energy and the time, it is costly to shuffle data between the memory and the processing entity, and for data‐intensive applications associated with artificial intelligence, the demand is ever increasing. A paradigm shift in traditional architectures is required, and in‐memory computing is one of the non‐von‐Neumann computing strategies. By harnessing physical signatures of the memory, computing workloads are administered in the same memory element. For in‐memory computing, a wide range of memristive material (MM) systems have been examined. Moreover, developing computing schemes that perform in the same sensory network and that minimize the data shuffle between the processing unit and the sensing element is a requirement, to process large volumes of data efficiently and decrease the energy consumption. In this review, an overview of the switching character and system signature harnessed in three archetypal MM systems is rendered, along with an integrated application survey for developing in‐sensor and in‐memory computing, viz., brain‐inspired or analogue computing, physical unclonable functions, and random number generators. The recent progress in theoretical studies that reveal the structural origin of the fast‐switching ability of the MM system is further summarized.

Funder

Ministry of Education, Singapore

Publisher

Wiley

Reference536 articles.

1. Memristors Enabled Computing Correlation Parameter In-Memory System: A Potential Alternative to Von Neumann Architecture

2. A.Patel F.Afram S.Chen K.Ghose inProc. of the 48th Design Automation Conf. ACM New York2011 pp.1050–1055.

3. M.Horowitz in2014 IEEE Int. Solid-State Circuits Conf. Dig. Tech. Pap. (ISSCC) IEEE Piscataway NJ USA2014 pp.10–14.

4. Memory devices and applications for in-memory computing

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