Journal Article

FZJ-2022-04073

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Dynamical memristors for higher-complexity neuromorphic computing

Kumar, S. ; Wang, X. ; Strachan, J. P. ; Yang, Y. ; Lu, W. D.

2022
Nature Publishing Group Basingstoke

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Please use a persistent id in citations: doi:10.1038/s41578-022-00434-z

Abstract: Research on electronic devices and materials is currently driven by both the slowing down of transistor scaling and the exponential growth of computing needs, which make present digital computing increasingly capacity-limited and power-limited. A promising alternative approach consists in performing computing based on intrinsic device dynamics, such that each device functionally replaces elaborate digital circuits, leading to adaptive ‘complex computing’. Memristors are a class of devices that naturally embody higher-order dynamics through their internal electrophysical processes. In this Review, we discuss how novel material properties enable complex dynamics and define different orders of complexity in memristor devices and systems. These native complex dynamics at the device level enable new computing architectures, such as brain-inspired neuromorphic systems, which offer both high energy efficiency and high computing capacity.

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Contributing Institute(s):

1. Neuromorphic Compute Nodes (PGI-14)

Research Program(s):

1. 5234 - Emerging NC Architectures (POF4-523) (POF4-523)

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Database coverage:
; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Essential Science Indicators ; IF >= 70 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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