Resistive-RAM-Based In-Memory Computing for Neural Network: A Review

Chen, Weijian; Qi, Zhi; Akhtar, Zahid; Siddique, Kamran

doi:10.3390/electronics11223667

Cited by 18 publications

(2 citation statements)

References 78 publications

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“…Furthermore, ReRAM’s compatibility with existing CMOS fabrication processes makes it a promising candidate for next-generation memory devices. ReRAM has been shown to have potential applications in neuromorphic computing [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Intermittent-Aware Design Exploration of Systolic Array Using Various Non-Volatile Memory: A Comparative Study

Taheri,

Tabrizchi,

Roohi

2024

Micromachines

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This paper conducts a comprehensive study on intermittent computing within IoT environments, emphasizing the interplay between different dataflows—row, weight, and output—and a variety of non-volatile memory technologies. We then delve into the architectural optimization of these systems using a spatial architecture, namely IDEA, with their processing elements efficiently arranged in a rhythmic pattern, providing enhanced performance in the presence of power failures. This exploration aims to highlight the diverse advantages and potential applications of each combination, offering a comparative perspective. In our findings, using IDEA for the row stationary dataflow with AlexNet on the CIFAR10 dataset, we observe a power efficiency gain of 2.7% and an average reduction of 21% in the required cycles. This study elucidates the potential of different architectural choices in enhancing energy efficiency and performance in IoT systems.

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Section: Introductionmentioning

confidence: 99%

Intermittent-Aware Design Exploration of Systolic Array Using Various Non-Volatile Memory: A Comparative Study

Taheri,

Tabrizchi,

Roohi

2024

Micromachines

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“…That being said, the memristor, proposed by Professor Cai and manufactured by HP Lab [2], has the characteristics of a small size and a low power consumption, as well as the advantages of nonlinearity and non-volatility. The realization of brain-like intelligence based on memristors can greatly promote the development of artificial neural networks for associative memory [3][4][5][6][7][8][9][10][11][12] and emotional intelligence [13][14][15][16][17][18][19]. With the rapid development of artificial intelligence, more and more people are paying attention to the research on brain-like emotional intelligence [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Multimodal Mood Consistency and Mood Dependency Neural Network Circuit Based on Memristors

Wang

Sun²,

Wang³

et al. 2023

Electronics

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The factors that affect learning efficiency in different environments have been considered in many studies, but multimode mood-consistent learning has not been considered specifically. In this paper, a neural network circuit based on memristors to determine multimode mood consistency and mood dependency was constructed. The circuit is composed of a voltage control module, an emotion module, and a synaptic neuron module. Through the voltage control module and emotion module, learning materials with different properties are input into the synaptic neurons. The learning efficiency of different learning materials under different emotions was analyzed in detail. A dual-channel mood-consistent learning was realized, and the mood dependency was further considered. The influence of different channels on the learning was studied to provide ideas for the future development of intelligent brain-like neural networks.

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Atomic Layer Deposition Films for Resistive Random‐Access Memories

Hao,

Peng,

Zierold

et al. 2024

Adv Materials Technologies

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Resistive random‐access memory (RRAM) stands out as a promising memory technology due to its ease of operation, high speed, affordability, exceptional stability, and potential to enable smaller memory devices with sizes under 10 nm. This has drawn significant attention, with atomic layer deposition (ALD) emerging as an ideal technology to tackle the challenges of nanoscale fabrication in the micro‐ and nanomanufacturing industry. ALD offers technological advantages such as functional multiple‐layer stacking, doping capabilities, and incorporating oxygen reservoirs or reactive layers. These factors contribute to achieving more intriguing, stable, and reliable nonvolatile resistance switching behaviors in RRAM. Specifically, ALD greatly benefits RRAM, that relies on the valence change mechanism, where high‐k transition metal oxides are commonly used as switching materials, and precise control over oxygen vacancies is achievable. This review provides a comprehensive overview of ALD films used in RRAM, delves into resistive switching properties and microscopic mechanisms in binary and ternary oxides and nitrides, and explores the impact of ALD‐prepared electrodes. Furthermore, the current status and future prospects of ALD‐based RRAM are highlighted, which is poised to catalyze further advancements in the fields of information storage and neural networks.

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Resistive-RAM-Based In-Memory Computing for Neural Network: A Review

Cited by 18 publications

References 78 publications

Intermittent-Aware Design Exploration of Systolic Array Using Various Non-Volatile Memory: A Comparative Study

Intermittent-Aware Design Exploration of Systolic Array Using Various Non-Volatile Memory: A Comparative Study

Multimodal Mood Consistency and Mood Dependency Neural Network Circuit Based on Memristors

Atomic Layer Deposition Films for Resistive Random‐Access Memories

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