A fully integrated reprogrammable memristor–CMOS system for efficient multiply–accumulate operations

Cai, Fuxi; Correll, Justin M.; Lee, Seung Hwan; Lim, Yong; Bothra, Vishishtha; Zhang, Zhengya; Flynn, Michael P.; Lü, Wei

doi:10.1038/s41928-019-0270-x

Cited by 519 publications

(363 citation statements)

References 45 publications

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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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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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“…Memristive systems in the broader sense, like resistive random-access memories (ReRAMs) or even phase-change memories (PCMs), 3 are progressing towards commercial application [118,119]. Aside from memory, memristors are also interesting for in-memory computing, neuromorphic computing, and reconfigurable logic [115,120,121].…”

Section: Memristorsmentioning

confidence: 99%

Hardware Security For and Beyond CMOS Technology

Knechtel¹

2020

Proceedings of the 2020 International Symposium on Physical Design

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As with most aspects of electronic systems and integrated circuits, hardware security has traditionally evolved around the dominant CMOS technology. However, with the rise of various emerging technologies, whose main purpose is to overcome the fundamental limitations for scaling and power consumption of CMOS technology, unique opportunities arise also to advance the notion of hardware security. In this paper, I first provide an overview on hardware security in general. Next, I review selected emerging technologies, namely (i) spintronics, (ii) memristors, (iii) carbon nanotubes and related transistors, (iv) nanowires and related transistors, and (v) 3D and 2.5D integration. I then discuss their application to advance hardware security and also outline related challenges. CCS CONCEPTS• Security and privacy → Security in hardware; • Hardware → Emerging technologies.

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“…However, state‐of‐the‐art techniques based on artificial neural networks are running with traditional computers based on the von Neumann architecture, leading to long‐processing latency and high energy consumption. In contrast with the von Neumann architecture using a separated central processing unit (CPU) and memory part, the neuromorphic computing directly implements memory and complex computations simultaneously in a brain‐like fashion …”

Section: Introductionmentioning

confidence: 99%

Recent Progress in Synaptic Devices Based on 2D Materials

Sun

Wang

Yang

2020

Advanced Intelligent Systems

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Diverse synaptic plasticity with a wide range of timescales in biological synapses plays an important role in memory, learning, and various signal processing with exceptionally low power consumption. Emulating biological synaptic functions by electric devices for neuromorphic computation has been considered as a way to overcome the traditional von Neumann architecture in which separated memory and information processing units require high power consumption for their functions. Synaptic devices are expected to conduct complex signal processing such as image classification, decision‐making, and pattern recognition in artificial neural networks. Among various materials and device architectures for synaptic devices, 2D materials and their van der Waals (vdW) heterostructures have been attracting tremendous attention from researchers based on their capacity to mimic unique synaptic plasticity for neuromorphic computing. Herein, the basic operations of biological synapses and physical properties of 2D materials are discussed, and then 2D materials and their vdW heterostructures for advanced synaptic operations with novel working mechanisms are reviewed. In particular, there is a focus on how to design synaptic devices with the vdW structures in terms of critical 2D materials and their limitations, providing insight into the emerging synaptic device systems and artificial neural networks with 2D materials.

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A fully integrated reprogrammable memristor–CMOS system for efficient multiply–accumulate operations

Cited by 519 publications

References 45 publications

Complementary Metal‐Oxide Semiconductor and Memristive Hardware for Neuromorphic Computing

Complementary Metal‐Oxide Semiconductor and Memristive Hardware for Neuromorphic Computing

Hardware Security For and Beyond CMOS Technology

Recent Progress in Synaptic Devices Based on 2D Materials

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