Memory devices and applications for in-memory computing

By Abu Sebastian, M. Le Gallo, R. Khaddam-Aljameh, Evangelos Eleftheriou. Nature Nanotechnology, 2020

2,015 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/0ff02cddd42e0434ff0e768ab1117ff326e3db6c

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In Cellular and Molecular Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Neuro-inspired computing chips (2020, background): “Neuro-inspired computing chips emulate the structure and working principles of the biological brain, and offer a promising approach to the development of intelligent computing 4,5 .”
A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference (2022, methodology): “crossbar array, (2) current DAC-based programming unit, (3) PWM-based input”
Hardware implementation of memristor-based artificial neural networks (2024, neutral): “In a DNN the information is represented with values that are continuous in time and can achieve high data recognition accuracy by using at least two layers of nonlinear neurons interconnected by adjustable synaptic weights 25 .”
Neuromorphic computing at scale (2025, positive): “Many emerging devices such as RRAM 107 , spintronic devices 154 , ferroelectric transistors 155 and phase-change memory 106 have been recently proposed and used in such neuromorphic systems.”
Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks (2022, background): “Furthermore, practical in-memory computing is challenging due to low computational precision resulting from various sources of nonidealities in the NVM devices such as intrinsic randomness, noise, and variability [11].”
Memristive technologies for data storage, computation, encryption, and radio-frequency communication (2022, background): “This IMC computational paradigm is finding a range of applications including scientific computing and deep learning ( 11 , 12 ).”
A crossbar array of magnetoresistive memory devices for in-memory computing (2022, background): “A prominent example is in-memory computing architectures based on the memory crossbar array 4–7 .”
In-memory hyperdimensional computing (2019, background): “The manipulation of large patterns stored in memory and its inherent robustness make HDC particularly well suited to emerging computing paradigms such as in-memory computing or computational memory based on emerging nanoscale resistive memory or memristive devices 19–23 .”