Memory devices and applications for in-memory computing

Abu Sebastian; Manuel Le Gallo; Riduan Khaddam-Aljameh; Evangelos Eleftheriou

doi:10.1038/s41565-020-0655-z

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

Affiliated Institutions

IBM Research - Zurich CH

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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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APA Style

                            
                                
                                    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%