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.

Keywords

In-Memory ProcessingComputer scienceVon Neumann architectureUnconventional computingComputing with MemoryKey (lock)Cognitive computingComputer architectureParallel computingDistributed computingMemory managementSemiconductor memoryFlat memory modelComputer hardwareCognition

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Publication Info

Year
2020
Type
review
Volume
15
Issue
7
Pages
529-544
Citations
1838
Access
Closed

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Cite This

Abu Sebastian, Manuel Le Gallo, Riduan Khaddam-Aljameh et al. (2020). Memory devices and applications for in-memory computing. Nature Nanotechnology , 15 (7) , 529-544. https://doi.org/10.1038/s41565-020-0655-z

Identifiers

DOI
10.1038/s41565-020-0655-z
PMID
32231270

Data Quality

Data completeness: 77%