Networking retinomorphic sensor with memristive crossbar for brain-inspired visual perception

Wang, Shuang; Wang, Chenyu; Wang, Pengfei; Wang, Cong; Li, Zhuan; Pan, Chen; Dai, Yitong; Gao, Anyuan; Liu, Chuan; Liu, Jian; Yang, Huafeng; Liu, Xiaowei; Cheng, Bin; Chen, Kunji; Wang, Zhenlin; Watanabe, Kenji; Taniguchi, Takashi; Liang, Shi‐Jun; Miao, Feng

doi:10.1093/nsr/nwaa172

Cited by 85 publications

(53 citation statements)

References 52 publications

(27 reference statements)

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“…ANNs from NJU: Professor Feng Miao and Professor Shijun Liang's group invented an integrated sensing−processing system consisting of retinomorphic sensors made of WSe 2 /h‐BN/Al 2 O 3 heterostructure and Pt/Ta/HfO x /Pt RRAM 1T1R crossbars, which implement a fully connected ANN and a recurrent ANN for letter recognition and object tracking. [ 304 ]…”

Section: Examples Of 1t1r Crossbarsmentioning

confidence: 99%

Memristive Crossbar Arrays for Storage and Computing Applications

Wang

Zhang

et al. 2021

Advanced Intelligent Systems

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The emergence of memristors with potential applications in data storage and artificial intelligence has attracted wide attentions. Memristors are assembled in crossbar arrays with data bits encoded by the resistance of individual cells. Despite the proposed high density and excellent scalability, the sneak‐path current causing cross interference impedes their practical applications. Therefore, developing novel architectures to mitigate sneak‐path current and improve efficiency, reliability, and stability may benefit next‐generation storage‐class memory (SCM). Moreover, conventional digital computers face the von‐Neumann bottleneck and the slowdown of transistors’ scaling, imposing a big challenge to hardware artificial intelligence. Memristive crossbar features colocation of memory and processing units, as well as superior scalability, making it a promising candidate for hardware accelerating machine learning and neuromorphic computing. Herein, first, crossbar architecture is introduced. Then, for storage, the origin of sneak‐path current is reviewed and techniques to mitigate this issue from the angle of materials and circuits are discussed. Computing wise, the applications of memristive crossbars in both machine learning and neuromorphic computing are surveyed, focusing on the structure of unit cells, the network topology, and the learning types. Finally, a perspective on future engineering and applications of memristive crossbars is discussed.

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Section: Examples Of 1t1r Crossbarsmentioning

confidence: 99%

Memristive Crossbar Arrays for Storage and Computing Applications

Wang

Zhang

et al. 2021

Advanced Intelligent Systems

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“…(A) Connecting 2D photodetectors with the memristor array is an effective way to improve energy efficiency. (B) Flowchart briefly illustrates the sensing and processing of images by a detector combined with a memristor array 4 . (C) 2DM photodetector with adjustable response rate is realized by adjusting 2DM band with double back gates.…”

Section: New Architecture For Machine Vision Systemsmentioning

confidence: 99%

Section: New Architecture For Machine Vision Systemsmentioning

confidence: 99%

“…With the rapid development of information integration, machine vision has become indispensable in automated production, autonomous driving, robotics, security, and other domains 2 . New application scenarios and increasing quantities of data have created a demand for MVSs with faster parallel processing, higher energy efficiency, smaller volume, and lower price 4 …”

Section: Introductionmentioning

confidence: 99%

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Next‐generation machine vision systems incorporating two‐dimensional materials: Progress and perspectives

Zhu

et al. 2021

InfoMat

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Machine vision systems (MVSs) are an important component of intelligent systems, such as autonomous vehicles and robots. However, with the continuous increase in data and new application scenarios, new requirements are put forward for the next generation of MVS. There is an urgent need to find new material systems to complement the existing semiconductor technology based on thin‐film materials, and new architectures must be explored to improve efficiency. Because of their unique physical properties, two‐dimensional (2D) materials have received extensive attention for use in MVSs, especially in biomimetic ones: the human visual system, which can process complex visual information with low power consumption, provides a model for next‐generation MVSs. This review paper summarizes the progress and challenges of applying 2D material photodetectors in sense‐memory‐computational integration and biomimetic image sensors for machine vision.

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“…So far, a variety of neuromorphic visual systems have been developed for implementing typical image processing functionalities including the contrast enhancement, noise suppression, adaptive imaging, recognition, and auto-encoding [5][6][7][8][9][10][11][12][13][14][15] . Among these systems, reconfigurable photosensor network (PS-NET) with an in-sensor computing architecture (Fig.…”

Section: Introductionmentioning

confidence: 99%

Ferroelectric photosensor network: an advanced hardware solution to real-time machine vision

Cui¹,

Fan²,

Li³

et al. 2021

Preprint

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Nowadays the development of machine vision is oriented toward real-time applications such as autonomous driving. This demands a hardware solution with low latency, high energy efficiency, and good reliability. Here, we demonstrate a robust and self-powered in-sensor computing paradigm with a ferroelectric photosensor network (FE-PS-NET). The FE-PS-NET, constituted by ferroelectric photosensors (FE-PSs) with tunable photoresponsivities, is capable of simultaneously capturing and processing images. In each FE-PS, self-powered photovoltaic responses, modulated by remanent polarization of an epitaxial ferroelectric Pb(Zr0.2Ti0.8)O3 layer, show not only multiple nonvolatile levels but also a sign reversibility, enabling the representation of a signed weight in a single device and hence reducing the hardware overhead for network construction. With multiple FE-PSs wired together, the FE-PS-NET acts on its own as an artificial neural network. It is demonstrated that an in situ multiply-accumulate operation between an input image and a stored photoresponsivity matrix is available in our FE-PS-NET hardware. The FE-PS-NET hardware is faultlessly competent for real-time image processing functionalities, including binary pattern classification with an accuracy of 100% and edge detection with an F-Measure of 95.2%. This study highlights the great potential of ferroelectric photovoltaics as the hardware basis of real-time machine vision.

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Networking retinomorphic sensor with memristive crossbar for brain-inspired visual perception

Cited by 85 publications

References 52 publications

Memristive Crossbar Arrays for Storage and Computing Applications

Memristive Crossbar Arrays for Storage and Computing Applications

Next‐generation machine vision systems incorporating two‐dimensional materials: Progress and perspectives

Ferroelectric photosensor network: an advanced hardware solution to real-time machine vision

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