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Review
. 2020 Jul;15(7):529-544.
doi: 10.1038/s41565-020-0655-z. Epub 2020 Mar 30.

Memory devices and applications for in-memory computing

Abu Sebastian  1 , Manuel Le Gallo  2 , Riduan Khaddam-Aljameh  2 , Evangelos Eleftheriou  2
Affiliations
Review

Memory devices and applications for in-memory computing

Abu Sebastian et al. Nat Nanotechnol. 2020 Jul.

Erratum in

Abstract

Traditional von Neumann computing systems involve separate processing and memory units. However, data movement is costly in terms of time and energy and this problem is aggravated by the recent explosive growth in highly data-centric applications related to artificial intelligence. This calls for a radical departure from the traditional systems and one such non-von Neumann computational approach is in-memory computing. Hereby certain computational tasks are performed in place in the memory itself by exploiting the physical attributes of the memory devices. Both charge-based and resistance-based memory devices are being explored for in-memory computing. In this Review, we provide a broad overview of the key computational primitives enabled by these memory devices as well as their applications spanning scientific computing, signal processing, optimization, machine learning, deep learning and stochastic computing.

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