In-memory computing with emerging nonvolatile memory devices

Cheng, Caidie; Tiw, Pek Jun; Cai, Yimao; Yan, Xiaoqin; Yang, Yuchao; Huang, Ru

doi:10.1007/s11432-021-3327-7

Cited by 49 publications

(17 citation statements)

References 237 publications

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“…These problems can be linked to the Ising model, a statistical physics approach that utilizes the behavior of interacting magnetic spins to explain ferromagnetism. [ 24,64–67 ]…”

Section: Resultsmentioning

confidence: 99%

“…These problems can be linked to the Ising model, a statistical physics approach that utilizes the behavior of interacting magnetic spins to explain ferromagnetism. [24,[64][65][66][67] The oscillator Ising model is a specific Ising Hamilton solver that could handle continuous-time problems, in contrast to discrete-time Ising model solvers like the Hopfield network. As a result, the oscillator Ising model can generate solutions to combinatorial optimization problems within a short period of the oscillator.…”

Section: A Coupled Oscillator-based Ising Hamiltonian Solvermentioning

confidence: 99%

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Harnessing Metal/Oxide Interlayer to Engineer the Memristive Response and Oscillation Dynamics of Two‐Terminal Memristors

Nath,

Sun,

Nandi

et al. 2023

Adv Funct Materials

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Devices with volatile memristive switching and self‐sustained relaxation oscillations have received significant attention for their use in neuromorphic computing and solving optimization problems. However, obtaining devices with stable response and reliable oscillation remains challenging. This work describes the utility of metal/oxide interlayers in achieving reliable device performance with tuneable characteristics using Nb‐Nb2O5‐Pt structures. Detailed physical characterization of the Nb/Nb2O5 interlayer region shows that it consists of a thin, near‐stoichiometric Nb2O5 and an NbOx layer with a graded oxygen content. The impact of these interlayers on device performance is assessed by investigating their switching characteristics and oscillation dynamics. It is shown that the presence of the interlayer has a direct impact on the memristive switching mode and parameters threshold switching characteristics parameters. These dependencies are explained with reference to a lumped‐element circuit model of the device and shown to be dominated by changes in device resistance caused by interlayer formation. Significantly, stable oscillation with tuneable frequency is obtained by increasing the thickness of the reactive Nb electrode. Then, by simulating an array of coupled oscillators, a unique Hamiltonian solver is demonstrated. These results provide pathways for optimizing volatile memristive switching characteristics and guidance on how such devices can be employed to solve combinatorial optimization problems.

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“…These problems can be linked to the Ising model, a statistical physics approach that utilizes the behavior of interacting magnetic spins to explain ferromagnetism. [ 24,64–67 ]…”

Section: Resultsmentioning

confidence: 99%

Section: A Coupled Oscillator-based Ising Hamiltonian Solvermentioning

confidence: 99%

Harnessing Metal/Oxide Interlayer to Engineer the Memristive Response and Oscillation Dynamics of Two‐Terminal Memristors

Nath,

Sun,

Nandi

et al. 2023

Adv Funct Materials

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“…[4][5][6] The focus on the density of memory and speed of processing elements has created a big discrepancy between processing speed and memory access, which is referred to as the memory wall. [7,8] Memory walls became even worse for new emerging applications such as AI and big data, which are data intensive. [9,10] At the same time, aggressive requirements on processing energy and real-time response require a new computing paradigm with possible new devices and architecture.…”

Section: Introductionmentioning

confidence: 99%

Advancement in Soft Iontronic Resistive Memory Devices and Their Application for Neuromorphic Computing

Khan

Kim

Chougale

et al. 2022

Advanced Intelligent Systems

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The aqueous electrolyte can be a deformable and stretchable liquid material for iontronic resistive memory devices. An aqueous medium makes a device closer to the brain‐like system with the movement of ions. This review paper proposes advances in liquid resistive memories and neuromorphic computing behavior to emulate electronic synapses. Primarily, the aqueous iontronic resistive memories can be used to study electrode and active layer materials and different device structures. Hence, herein, a timely and comprehensive study of these devices using ionic liquids, hydrogels, salt solutions, and soft electrodes to classify the device mechanism is presented. The filament formation is discussed in detail based on ion concentration polarization, electrode metallization, and movements of ions and charged molecules, which result in the formation of the metal dendrite. To manufacture a higher‐performance memory, device parameters should be optimized based on aqueous electrolytes, electrode materials, and other device design parameters. Aqueous electrolytes have smooth neurotransmission ability to fabricate brain‐inspired resistive memories with stable performance and device repeatability with smooth ion transmission. Aqueous electrode materials can be reliable for neural interface activities to compute electronic synapsis with electrical and chemical properties to ensure device reliability for a longer time period.

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“…Memristive device crossbar arrays are thereby power-efficient and fast in the calculation of VMMs and LCAs. Memristive devices offer the fascinating possibility of in-memory computing and neuromorphic computing, thus bypassing the restrictions of computing speed as well as the power consumption of the von Neumann architecture because of the distanced memory and processing units [55][56][57][58].…”

Section: Introductionmentioning

confidence: 99%

Memristive devices based hardware for unlabeled data processing

Xiao

Yan

Zhang

et al. 2022

Neuromorph. Comput. Eng.

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Unlabeled data processing is of great significance for artificial intelligence (AI), since well-structured labelled data are scarce in a majority of practical applications due to the high cost of human annotation of labeling data. Therefore, automatous analysis of unlabeled datasets is important, and relevant algorithms for processing unlabeled data, such as k-means clustering, restricted Boltzmann machine and locally competitive algorithms etc., play a critical role in the development of AI techniques. Memristive devices offer potential for power and time efficient implementation of unlabeled data processing due to their unique properties in neuromorphic and in-memory computing. This review provides an overview of the design principles and applications of memristive devices for various unlabeled data processing and cognitive AI tasks.

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In-memory computing with emerging nonvolatile memory devices

Cited by 49 publications

References 237 publications

Harnessing Metal/Oxide Interlayer to Engineer the Memristive Response and Oscillation Dynamics of Two‐Terminal Memristors

Harnessing Metal/Oxide Interlayer to Engineer the Memristive Response and Oscillation Dynamics of Two‐Terminal Memristors

Advancement in Soft Iontronic Resistive Memory Devices and Their Application for Neuromorphic Computing

Memristive devices based hardware for unlabeled data processing

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