Integrating memristors and CMOS for better AI

Jiang, Weiwen; Xie, Bike; Chen, Wei; Shi, Yiyu

doi:10.1038/s41928-019-0307-1

Cited by 19 publications

(9 citation statements)

References 6 publications

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“…The crossbar array can conduct parallel operations for matrix multiplication and addition, which can accelerate neural network computation. Therefore, the neural network based on memristors provides a solution for the construction of a new computing architecture that combines storage and computing [246][247][248]. The oxides-based memristive array has been widely reported [30,[249][250][251][252].…”

Section: Neuromorphic Device Arraymentioning

confidence: 99%

2D materials and van der Waals heterojunctions for neuromorphic computing

Zhang

Yang

et al. 2022

Neuromorph. Comput. Eng.

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Neuromorphic computing systems employing artificial synapses and neurons are expected to overcome the limitations of the present von Neumann computing architecture in terms of efficiency and bandwidth limits. Traditional neuromorphic devices have used 3D bulk materials, and thus, the resulting device size is difficult to be further scaled down for high density integration, which is required for highly integrated parallel computing. The emergence of two-dimensional (2D) materials offers a promising solution, as evidenced by the surge of reported 2D materials functioning as neuromorphic devices for next-generation computing. In this review, we summarize the 2D materials and their heterostructures to be used for neuromorphic computing devices, which could be classified by the working mechanism and device geometry. Then, we survey neuromorphic device arrays and their applications including artificial visual, tactile, and auditory functions. Finally, we discuss the current challenges of 2D materials to achieve practical neuromorphic devices, providing a perspective on the improved device performance, and integration level of the system. This will deepen our understanding of 2D materials and their heterojunctions and provide a guide to design highly performing memristors. At the same time, the challenges encountered in the industry are discussed, which provides a guide for the development direction of memristors.

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Section: Neuromorphic Device Arraymentioning

confidence: 99%

2D materials and van der Waals heterojunctions for neuromorphic computing

Zhang

Yang

et al. 2022

Neuromorph. Comput. Eng.

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“…There have been many previous hybrid CMOS–memristor neuromorphic designs in the literature . In most of these systems, the neuron and interfacing circuitry are designed in CMOS, whereas synapses implementing targeted synaptic plasticity rules, such as STDP, are realized using one or a few memristors, which are programmed through shared or individual CMOS circuits.…”

Section: Neuromorphic Components Designmentioning

confidence: 99%

Complementary Metal‐Oxide Semiconductor and Memristive Hardware for Neuromorphic Computing

Azghadi

Chen

Eshraghian

et al. 2020

Advanced Intelligent Systems

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The ever‐increasing processing power demands of digital computers cannot continue to be fulfilled indefinitely unless there is a paradigm shift in computing. Neuromorphic computing, which takes inspiration from the highly parallel, low‐power, high‐speed, and noise‐tolerant computing capabilities of the brain, may provide such a shift. Many researchers from across academia and industry have been studying materials, devices, circuits, and systems, to implement some of the functions of networks of neurons and synapses to develop neuromorphic computing platforms. These platforms are being designed using various hardware technologies, including the well‐established complementary metal‐oxide semiconductor (CMOS), and emerging memristive technologies such as SiOx‐based memristors. Herein, recent progress in CMOS, SiOx‐based memristive, and mixed CMOS‐memristive hardware for neuromorphic systems is highlighted. New and published results from various devices are provided that are developed to replicate selected functions of neurons, synapses, and simple spiking networks. It is shown that the CMOS and memristive devices are assembled in different neuromorphic learning platforms to perform simple cognitive tasks such as classification of spike rate‐based patterns or handwritten digits. Herein, it is envisioned that what is demonstrated is useful to the unconventional computing research community by providing insights into advances in neuromorphic hardware technologies.

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“…From the perspective of hardware implementation, it is mandatory to explore artificial synapse and neuron for neuromorphic systems [5,6]. Memristor, known as the fourth basic circuit element, has been investigated to implement artificial synapses and neurons due to its nanoscale, non-volatile memorability, and nonlinearity characteristics [7][8][9][10][11]. To date, reversible artificial synapses have been reported using resistive switching memristors [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neurons Based on Ag/V2C/W Threshold Switching Memristors

et al. 2021

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Artificial synapses and neurons are two critical, fundamental bricks for constructing hardware neural networks. Owing to its high-density integration, outstanding nonlinearity, and modulated plasticity, memristors have attracted emerging attention on emulating biological synapses and neurons. However, fabricating a low-power and robust memristor-based artificial neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a single two-dimensional (2D) MXene(V2C)-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, originating from the Ag diffusion-based filamentary mechanism. Moreover, our V2C-based artificial neurons faithfully achieve multiple neural functions including leaky integration, threshold-driven fire, self-relaxation, and linear strength-modulated spike frequency characteristics. This work demonstrates that three-atom-type MXene (e.g., V2C) memristors may provide an efficient method to construct the hardware neuromorphic computing systems.

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Integrating memristors and CMOS for better AI

Cited by 19 publications

References 6 publications

2D materials and van der Waals heterojunctions for neuromorphic computing

2D materials and van der Waals heterojunctions for neuromorphic computing

Complementary Metal‐Oxide Semiconductor and Memristive Hardware for Neuromorphic Computing

Artificial Neurons Based on Ag/V2C/W Threshold Switching Memristors

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