Controlled Majority-Inverter Graph Logic With Highly Nonlinear, Self-Rectifying Memristor

Ni, Run; Ling, Yang; Huang, Xiaodi; Ren, Shengguang; Wan, Tianqing; Li, Yang; Miao, Xiangshui

doi:10.1109/ted.2021.3106234

Cited by 17 publications

(22 citation statements)

References 30 publications

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“…The SRM is well-favoured by researchers for effectively suppressing the sneak path in the crossbar arrays and is considered an effective solution for high-density integration (with a 4F 2 footprint and 3D scalability) owing to their inherent reverse self-rectifying characteristics. Furthermore, because of their ultra-low operating current (down to pA level), SRM shows great potential for implementing low-powerconsumption mIMC hardware [26][27][28][29] . Our SRM cells were fabricated with a vertical stack structure of Pt/HfO 2 /TaO x /Ta (Figure 2a), and the details of the device structure analysis using transmission electron microscopes (TEM), energy dispersive spectroscopy (EDS) and the X-ray photoelectron spectroscopy (XPS) are shown in Supplementary Note 1.…”

Section: Resultsmentioning

confidence: 99%

“…It was con rmed that our SRM cell can provide a large inhibit region (− 2 V -1 V) with an extremely low leakage current (< 0.1 pA), which can block the sneak current owing through unselected cells in the passive array 26 . Additionally, a low reset current (< 1 pA) is allowed, which rivals the best records of prior state-of-the-art studies 27,28 . Figure 2e demonstrates the excellent retention of both the low resistance state (LRS) and high resistance state (HRS) under 125 °C temperature, measured by a 2 V read voltage for over 3 × 10 4 s. Remarkably, the operating current of the SRM cell can be as low as 10 pA, under which the device still demonstrates stable resistive switching with considerable NL (> 100) and RR (> 20) (Supplementary Figure 4b and Figure 4c).…”

Section: Resultsmentioning

confidence: 99%

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In-memory sparse matrix multiplication with a low-power self-rectifying memristor array

Ren

et al. 2022

Preprint

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Memristor-enabled in-memory computing provides an unconventional computing paradigm to surpass the energy efficiency of von Neumann computers. However, owing to the physical limitation of the crossbar structure, although the memristor array is desirable for dense computation, it suffers from significant performance degradation in both energy and area efficiency when processing sparse linear algebra operations. In this work, we report a highly efficient in-memory sparse computing system based on the self-rectifying memristor, which originates from the joint effort of devices and algorithms and is used to solve computational modelling problems. This system is expected to have 74.9 – 19.6 TOPS / W energy efficiency for 2-bit to 8-bit sparse computation in computational modelling tasks. Compared to the previous in-memory computing hardware, our system provides over one order of magnitude improvement in energy efficiency with more than two orders of magnitude reduction in hardware overhead. This work could pave the road towards a highly efficient, unconventional computing solution for high-performance computing.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

In-memory sparse matrix multiplication with a low-power self-rectifying memristor array

Ren

et al. 2022

Preprint

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“…As demonstrated in Figure 7(e) , our research group fabricated a memristor with TiN/ITO structure, which demonstrated a self-compliance current effect for the ITO electrode [ 164 ]. Another novel characteristic of memristor is self-rectifying effect，which can effectively suppress the crosstalk current and significantly simplify the complexity of circuit [ 168–175 ]. Besides, selector is often combined with memristor for 3D integration.…”

Section: Device Performancementioning

confidence: 99%

A review of memristor: material and structure design, device performance, applications and prospects

Xiao

Jiang

Chen

et al. 2023

Science and Technology of Advanced Materials

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With the booming growth of artificial intelligence (AI), the traditional von Neumann computing architecture based on complementary metal oxide semiconductor devices are facing memory wall and power wall. Memristor based in-memory computing can potentially overcome the current bottleneck of computer and achieve hardware breakthrough. In this review, the recent progress of memory devices in material and structure design, device performance and applications are summarized. Various resistive switching materials, including electrodes, binary oxides, perovskites, organics, and two-dimensional materials, are presented and their role in the memristor are discussed. Subsequently, the construction of shaped electrodes, the design of functional layer and other factors influencing the device performance are analyzed. We focus on the modulation of the resistances and the effective methods to enhance the performance. Furthermore, synaptic plasticity, optical-electrical properties, the fashionable applications in logic operation and analog calculation are introduced. Finally, some critical issues such as the resistive switching mechanism, multi-sensory fusion, system-level optimization are discussed.

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“…Owing to its self-rectifying nature, the SRM can substantially outperform other nonvolatile memories, such as the phase-change memory (PCM) and resistive random-access memory (ReRAM), for high-density integration without additional selective devices [4F 2 footprint and prominent three-dimensional (3D) scalability]. Moreover, with their ultralow operating current (down to picoampere level), SRM shows great potential to construct energy-efficient mIMC systems for data-intensive tasks (27)(28)(29)(30)(31)(32)(33). Our SRM cells were fabricated with a vertical stack structure of Pt/HfO 2 /TaO x /Ta (Fig.…”

Section: Characteristics Of the Srm Arraymentioning

confidence: 99%

Sparse matrix multiplication in a record-low power self-rectifying memristor array for scientific computing

Ren

et al. 2023

Sci. Adv.

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Memristor-enabled in-memory computing provides an unconventional computing paradigm to surpass the energy efficiency of von Neumann computers. Owing to the limitation of the computing mechanism, while the crossbar structure is desirable for dense computation, the system’s energy and area efficiency degrade substantially in performing sparse computation tasks, such as scientific computing. In this work, we report a high-efficiency in-memory sparse computing system based on a self-rectifying memristor array. This system originates from an analog computing mechanism that is motivated by the device’s self-rectifying nature, which can achieve an overall performance of ~97 to ~11 TOPS/W for 2- to 8-bit sparse computation when processing practical scientific computing tasks. Compared to previous in-memory computing system, this work provides over 85 times improvement in energy efficiency with an approximately 340 times reduction in hardware overhead. This work can pave the road toward a highly efficient in-memory computing platform for high-performance computing.

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Controlled Majority-Inverter Graph Logic With Highly Nonlinear, Self-Rectifying Memristor

Cited by 17 publications

References 30 publications

In-memory sparse matrix multiplication with a low-power self-rectifying memristor array

In-memory sparse matrix multiplication with a low-power self-rectifying memristor array

A review of memristor: material and structure design, device performance, applications and prospects

Sparse matrix multiplication in a record-low power self-rectifying memristor array for scientific computing

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