1
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Lai Q, Guo S. Heterogeneous coexisting attractors, large-scale amplitude control and finite-time synchronization of central cyclic memristive neural networks. Neural Netw 2024; 178:106412. [PMID: 38838394 DOI: 10.1016/j.neunet.2024.106412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 04/15/2024] [Accepted: 05/26/2024] [Indexed: 06/07/2024]
Abstract
Memristors are of great theoretical and practical significance for chaotic dynamics research of brain-like neural networks due to their excellent physical properties such as brain synapse-like memorability and nonlinearity, especially crucial for the promotion of AI big models, cloud computing, and intelligent systems in the artificial intelligence field. In this paper, we introduce memristors as self-connecting synapses into a four-dimensional Hopfield neural network, constructing a central cyclic memristive neural network (CCMNN), and achieving its effective control. The model adopts a central loop topology and exhibits a variety of complex dynamic behaviors such as chaos, bifurcation, and homogeneous and heterogeneous coexisting attractors. The complex dynamic behaviors of the CCMNN are investigated in depth numerically by equilibrium point stability analysis as well as phase trajectory maps, bifurcation maps, time-domain maps, and LEs. It is found that with the variation of the internal parameters of the memristor, asymmetric heterogeneous attractor coexistence phenomena appear under different initial conditions, including the multi-stable coexistence behaviors of periodic-periodic, periodic-stable point, periodic-chaotic, and stable point-chaotic. In addition, by adjusting the structural parameters, a wide range of amplitude control can be realized without changing the chaotic state of the system. Finally, based on the CCMNN model, an adaptive synchronization controller is designed to achieve finite-time synchronization control, and its application prospect in simple secure communication is discussed. A microcontroller-based hardware circuit and NIST test are conducted to verify the correctness of the numerical results and theoretical analysis.
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Affiliation(s)
- Qiang Lai
- School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, 330013, China.
| | - Shicong Guo
- School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, 330013, China
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2
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Jannesar N, Akbarzadeh-Sherbaf K, Safari S, Vahabie AH. SSTE: Syllable-Specific Temporal Encoding to FORCE-learn audio sequences with an associative memory approach. Neural Netw 2024; 177:106368. [PMID: 38761415 DOI: 10.1016/j.neunet.2024.106368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 03/28/2024] [Accepted: 05/05/2024] [Indexed: 05/20/2024]
Abstract
The circuitry and pathways in the brains of humans and other species have long inspired researchers and system designers to develop accurate and efficient systems capable of solving real-world problems and responding in real-time. We propose the Syllable-Specific Temporal Encoding (SSTE) to learn vocal sequences in a reservoir of Izhikevich neurons, by forming associations between exclusive input activities and their corresponding syllables in the sequence. Our model converts the audio signals to cochleograms using the CAR-FAC model to simulate a brain-like auditory learning and memorization process. The reservoir is trained using a hardware-friendly approach to FORCE learning. Reservoir computing could yield associative memory dynamics with far less computational complexity compared to RNNs. The SSTE-based learning enables competent accuracy and stable recall of spatiotemporal sequences with fewer reservoir inputs compared with existing encodings in the literature for similar purpose, offering resource savings. The encoding points to syllable onsets and allows recalling from a desired point in the sequence, making it particularly suitable for recalling subsets of long vocal sequences. The SSTE demonstrates the capability of learning new signals without forgetting previously memorized sequences and displays robustness against occasional noise, a characteristic of real-world scenarios. The components of this model are configured to improve resource consumption and computational intensity, addressing some of the cost-efficiency issues that might arise in future implementations aiming for compactness and real-time, low-power operation. Overall, this model proposes a brain-inspired pattern generation network for vocal sequences that can be extended with other bio-inspired computations to explore their potentials for brain-like auditory perception. Future designs could inspire from this model to implement embedded devices that learn vocal sequences and recall them as needed in real-time. Such systems could acquire language and speech, operate as artificial assistants, and transcribe text to speech, in the presence of natural noise and corruption on audio data.
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Affiliation(s)
- Nastaran Jannesar
- High Performance Embedded Architecture Lab., School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | | | - Saeed Safari
- High Performance Embedded Architecture Lab., School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
| | - Abdol-Hossein Vahabie
- Department of Psychology, Faculty of Psychology and Education, University of Tehran, Tehran, Iran; Cognitive Systems Laboratory, Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
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3
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Zeng T, Shi S, Hu K, Jia L, Li B, Sun K, Su H, Gu Y, Xu X, Song D, Yan X, Chen J. Approaching the Ideal Linearity in Epitaxial Crystalline-Type Memristor by Controlling Filament Growth. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2401021. [PMID: 38695721 DOI: 10.1002/adma.202401021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 04/29/2024] [Indexed: 05/15/2024]
Abstract
Brain-inspired neuromorphic computing has attracted widespread attention owing to its ability to perform parallel and energy-efficient computation. However, the synaptic weight of amorphous/polycrystalline oxide based memristor usually exhibits large nonlinear behavior with high asymmetry, which aggravates the complexity of peripheral circuit system. Controllable growth of conductive filaments is highly demanded for achieving the highly linear conductance modulation. However, the stochastic behavior of the filament growth in commonly used amorphous/polycrystalline oxide memristor makes it very challenging. Here, the epitaxially grown Hf0.5Zr0.5O2-based memristor with the linearity and symmetry approaching ideal case is reported. A layer of Cu nanoparticles is inserted into epitaxially grown Hf0.5Zr0.5O2 film to form the grain boundaries due to the breaking of the epitaxial growth. By combining with the local electric field enhancement, the growth of filament is confined in the grain boundaries due to the fact that the diffusion of oxygen vacancy in crystalline lattice is more difficult than that in the grain boundaries. Furthermore, the decimal operation and high-accuracy neural network are demonstrated by utilizing the highly linear and multi-level conductance modulation capacity. This method opens an avenue to control the filament growth for the application of resistance random access memory (RRAM) and neuromorphic computing.
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Affiliation(s)
- Tao Zeng
- Department of Materials Science and Engineering, National University of Singapore, Singapore, 117575, Singapore
| | - Shu Shi
- Department of Materials Science and Engineering, National University of Singapore, Singapore, 117575, Singapore
| | - Kejun Hu
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, China
| | - Lanxin Jia
- Department of Materials Science and Engineering, National University of Singapore, Singapore, 117575, Singapore
| | - Boyu Li
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, China
| | - Kaixuan Sun
- Department of Materials Science and Engineering, National University of Singapore, Singapore, 117575, Singapore
- Chongqing Research Institute, National University of Singapore, Chongqing, 401123, China
- School of Chemistry and Materials Science of Shanxi Normal University, Taiyuan, 030031, China
| | - Hanxin Su
- Department of Materials Science and Engineering, National University of Singapore, Singapore, 117575, Singapore
- Chongqing Research Institute, National University of Singapore, Chongqing, 401123, China
- School of Chemistry and Materials Science of Shanxi Normal University, Taiyuan, 030031, China
| | - Youdi Gu
- Department of Materials Science and Engineering, National University of Singapore, Singapore, 117575, Singapore
| | - Xiaohong Xu
- School of Chemistry and Materials Science of Shanxi Normal University, Taiyuan, 030031, China
| | - Dongsheng Song
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, China
| | - Xiaobing Yan
- College of Electron and Information Engineering, Hebei University, Baoding, 071002, China
| | - Jingsheng Chen
- Department of Materials Science and Engineering, National University of Singapore, Singapore, 117575, Singapore
- Chongqing Research Institute, National University of Singapore, Chongqing, 401123, China
- Suzhou Research Institute, National University of Singapore, Jiang Su, 215123, China
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4
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Wang H, Wang J, Yan S, Pan R, Sun M, Yu Q, Chen T, Chen L, Liu Y. Elementary cellular automata realized by stateful three-memristor logic operations. Sci Rep 2024; 14:2677. [PMID: 38302642 PMCID: PMC10834433 DOI: 10.1038/s41598-024-53125-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 01/29/2024] [Indexed: 02/03/2024] Open
Abstract
Cellular automata (CA) are computational systems that exhibit complex global behavior arising from simple local rules, making them a fascinating candidate for various research areas. However, challenges such as limited flexibility and efficiency on conventional hardware platforms still exist. In this study, we propose a memristor-based circuit for implementing elementary cellular automata (ECA) by extending the stateful three-memristor logic operations derived from material implication (IMP) logic gates. By leveraging the inherent physical properties of memristors, this approach offers simplicity, minimal operational steps, and high flexibility in implementing ECA rules by adjusting the circuit parameters. The mathematical principles governing circuit parameters are analyzed, and the evolution of multiple ECA rules is successfully demonstrated, showcasing the robustness in handling the stochastic nature of memristors. This approach provides a hardware solution for ECA implementation and opens up new research opportunities in the hardware implementation of CA.
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Affiliation(s)
- Hongzhe Wang
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Junjie Wang
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| | - Shiqin Yan
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Ruicheng Pan
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Mingyuan Sun
- China Changfeng Mechanics and Electronics Technology Academy, Beijing, 100039, China
| | - Qi Yu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Tupei Chen
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Lei Chen
- Beijing Microelectronics Technology Institute, Beijing, 100076, China
| | - Yang Liu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, China
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Wang P, Li J, Xue W, Ci W, Jiang F, Shi L, Zhou F, Zhou P, Xu X. Integrated In-Memory Sensor and Computing of Artificial Vision Based on Full-vdW Optoelectronic Ferroelectric Field-Effect Transistor. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2305679. [PMID: 38029338 PMCID: PMC10797471 DOI: 10.1002/advs.202305679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/01/2023] [Indexed: 12/01/2023]
Abstract
The development and application of artificial intelligence have led to the exploitation of low-power and compact intelligent information-processing systems integrated with sensing, memory, and neuromorphic computing functions. The 2D van der Waals (vdW) materials with abundant reservoirs for arbitrary stacking based on functions and enabling continued device downscaling offer an attractive alternative for continuously promoting artificial intelligence. In this study, full 2D SnS2 /h-BN/CuInP2 S6 (CIPS)-based ferroelectric field-effect transistors (Fe-FETs) and utilized light-induced ferroelectric polarization reversal to achieve excellent memory properties and multi-functional sensing-memory-computing vision simulations are designed. The device exhibits a high on/off current ratio of over 105 , long retention time (>104 s), stable cyclic endurance (>350 cycles), and 128 multilevel current states (7-bit). In addition, fundamental synaptic plasticity characteristics are emulated including paired-pulse facilitation (PPF), short-term plasticity (STP), long-term plasticity (LTP), long-term potentiation, and long-term depression. A ferroelectric optoelectronic reservoir computing system for the Modified National Institute of Standards and Technology (MNIST) handwritten digital recognition achieved a high accuracy of 93.62%. Furthermore, retina-like light adaptation and Pavlovian conditioning are successfully mimicked. These results provide a strategy for developing a multilevel memory and novel neuromorphic vision systems with integrated sensing-memory-processing.
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Affiliation(s)
- Peng Wang
- Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education & School of Chemistry and Materials ScienceShanxi Normal UniversityTaiyuan030031China
| | - Jie Li
- School of MicroelectronicsSouthern University of Science and TechnologyShenzhen518000China
| | - Wuhong Xue
- Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education & School of Chemistry and Materials ScienceShanxi Normal UniversityTaiyuan030031China
| | - Wenjuan Ci
- Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education & School of Chemistry and Materials ScienceShanxi Normal UniversityTaiyuan030031China
| | - Fengxian Jiang
- Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education & School of Chemistry and Materials ScienceShanxi Normal UniversityTaiyuan030031China
| | - Lei Shi
- Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education & School of Chemistry and Materials ScienceShanxi Normal UniversityTaiyuan030031China
| | - Feichi Zhou
- School of MicroelectronicsSouthern University of Science and TechnologyShenzhen518000China
| | - Peng Zhou
- ASIC & System State Key Lab School of MicroelectronicsFudan UniversityShanghai200433China
| | - Xiaohong Xu
- Key Laboratory of Magnetic Molecules and Magnetic Information Materials of Ministry of Education & School of Chemistry and Materials ScienceShanxi Normal UniversityTaiyuan030031China
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6
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Xu M, Chen X, Guo Y, Wang Y, Qiu D, Du X, Cui Y, Wang X, Xiong J. Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2301063. [PMID: 37285592 DOI: 10.1002/adma.202301063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/15/2023] [Indexed: 06/09/2023]
Abstract
Neuromorphic computing has been attracting ever-increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post-Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data-intensive computing in that domain. Reconfigurable neuromorphic computing, an on-demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain-inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, one comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration-level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities.
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Affiliation(s)
- Minyi Xu
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xinrui Chen
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yehao Guo
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yang Wang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Dong Qiu
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xinchuan Du
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yi Cui
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xianfu Wang
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Jie Xiong
- State Key Laboratory of Electronic Thin Film and Integrated Devices, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
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Yan X, Zheng Z, Sangwan VK, Qian JH, Wang X, Liu SE, Watanabe K, Taniguchi T, Xu SY, Jarillo-Herrero P, Ma Q, Hersam MC. Moiré synaptic transistor with room-temperature neuromorphic functionality. Nature 2023; 624:551-556. [PMID: 38123805 DOI: 10.1038/s41586-023-06791-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 10/26/2023] [Indexed: 12/23/2023]
Abstract
Moiré quantum materials host exotic electronic phenomena through enhanced internal Coulomb interactions in twisted two-dimensional heterostructures1-4. When combined with the exceptionally high electrostatic control in atomically thin materials5-8, moiré heterostructures have the potential to enable next-generation electronic devices with unprecedented functionality. However, despite extensive exploration, moiré electronic phenomena have thus far been limited to impractically low cryogenic temperatures9-14, thus precluding real-world applications of moiré quantum materials. Here we report the experimental realization and room-temperature operation of a low-power (20 pW) moiré synaptic transistor based on an asymmetric bilayer graphene/hexagonal boron nitride moiré heterostructure. The asymmetric moiré potential gives rise to robust electronic ratchet states, which enable hysteretic, non-volatile injection of charge carriers that control the conductance of the device. The asymmetric gating in dual-gated moiré heterostructures realizes diverse biorealistic neuromorphic functionalities, such as reconfigurable synaptic responses, spatiotemporal-based tempotrons and Bienenstock-Cooper-Munro input-specific adaptation. In this manner, the moiré synaptic transistor enables efficient compute-in-memory designs and edge hardware accelerators for artificial intelligence and machine learning.
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Affiliation(s)
- Xiaodong Yan
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
| | - Zhiren Zheng
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Vinod K Sangwan
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
| | - Justin H Qian
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
| | - Xueqiao Wang
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Stephanie E Liu
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA
| | - Kenji Watanabe
- Research Center for Functional Materials, National Institute for Materials Science, Tsukuba, Japan
| | - Takashi Taniguchi
- International Center for Material Nanoarchitectonics, National Institute for Materials Science, Tsukuba, Japan
| | - Su-Yang Xu
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | | | - Qiong Ma
- Department of Physics, Boston College, Chestnut Hill, MA, USA.
- CIFAR Azrieli Global Scholars Program, CIFAR, Toronto, Ontario, Canada.
| | - Mark C Hersam
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL, USA.
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA.
- Department of Chemistry, Northwestern University, Evanston, IL, USA.
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8
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Alexander P, Parastesh F, Hamarash II, Karthikeyan A, Jafari S, He S. Effect of the electromagnetic induction on a modified memristive neural map model. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:17849-17865. [PMID: 38052539 DOI: 10.3934/mbe.2023793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
The significance of discrete neural models lies in their mathematical simplicity and computational ease. This research focuses on enhancing a neural map model by incorporating a hyperbolic tangent-based memristor. The study extensively explores the impact of magnetic induction strength on the model's dynamics, analyzing bifurcation diagrams and the presence of multistability. Moreover, the investigation extends to the collective behavior of coupled memristive neural maps with electrical, chemical, and magnetic connections. The synchronization of these coupled memristive maps is examined, revealing that chemical coupling exhibits a broader synchronization area. Additionally, diverse chimera states and cluster synchronized states are identified and discussed.
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Affiliation(s)
- Prasina Alexander
- Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai, India
| | - Fatemeh Parastesh
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran
| | - Ibrahim Ismael Hamarash
- Electrical Engineering Department, Salahaddin University-Erbil, Kirkuk Rd., Erbil, Kurdistan, Iraq
- School of Computer Science and Engineering, University of Kurdistan Hewler, 40m St., Erbil, Kurdistan, Iraq
| | - Anitha Karthikeyan
- Department of Electronics and Communication Engineering, Vemu Institute of Technology, Chithoor, India
- Department of Electronics and Communications Engineering and University Centre for Research & Development, Chandigarh University, Mohali-140413, Punjab
| | - Sajad Jafari
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran
- Health Technology Research Institute, Amirkabir University of Technology (Tehran Polytechnic), Iran
| | - Shaobo He
- School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China
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9
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Xu C, Liao M, Wang C, Sun J, Lin H. Memristive competitive hopfield neural network for image segmentation application. Cogn Neurodyn 2023; 17:1061-1077. [PMID: 37522050 PMCID: PMC10374519 DOI: 10.1007/s11571-022-09891-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 09/06/2022] [Accepted: 09/18/2022] [Indexed: 11/30/2022] Open
Abstract
Image segmentation implementation provides simplified and effective feature information of image. Neural network algorithms have made significant progress in the application of image segmentation task. However, few studies focus on the implementation of hardware circuits with high-efficiency analog calculations and parallel operations for image segmentation problem. In this paper, a memristor-based competitive Hopfield neural network circuit is proposed to deal with the image segmentation problem. In this circuit, the memristive cross array is applied to store synaptic weights and perform matrix operations. The competition module based on the Winner-take-all mechanism is composed of the competition neurons and the competition control circuit, which simplifies the energy function of the Hopfield neural network and realizes the output function. Operational amplifiers and ABM modules are used to integrate operations and process external input information, respectively. Based on these designs, the circuit can automatically implement iteration and update of data. A series of PSPICE simulations are designed to verify the image segmentation capability of this circuit. Comparative experimental results and analysis show that this circuit has effective improvements both in processing speed and segmentation accuracy compared with other methods. Moreover, the proposed circuit shows good robustness to noise and memristive variation.
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Affiliation(s)
- Cong Xu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082 China
| | - Meiling Liao
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082 China
| | - Chunhua Wang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082 China
| | - Jingru Sun
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082 China
| | - Hairong Lin
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082 China
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10
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Gerasimova S, Lebedeva A, Gromov N, Malkov A, Fedulina А, Levanova T, Pisarchik A. Memristive Neural Networks for Predicting Seizure Activity. Sovrem Tekhnologii Med 2023; 15:30-38. [PMID: 38434190 PMCID: PMC10902902 DOI: 10.17691/stm2023.15.4.03] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Indexed: 03/05/2024] Open
Abstract
The aim of the study is to assess the possibilities of predicting epileptiform activity using the neuronal activity data recorded from the hippocampus and medial entorhinal cortex of mice with chronic epileptiform activity. To reach this goal, a deep artificial neural network (ANN) has been developed and its implementation based on memristive devices has been demonstrated. Materials and Methods The biological part of the investigation. Young healthy outbred CD1 mice were used in our study. They were divided into two groups: control (n=6) and the group with induced chronic epileptiform activity (n=6). Local field potentials (LFP) were recorded from the hippocampus and medial entorhinal cortex of the mice of both groups to register neuronal activity. The LFP recordings were used for deep ANN training. Epileptiform activity in mice was modeled by intraperitoneal injection of pilocarpine (280 mg/kg). LFP were recorded in the awake mice a month after the induction of epileptiform activity.Mathematical part of the investigation. A deep long short-term memory (LSTM) ANN capable of predicting biological signals of neuronal activity in mice has been developed. The ANN implementation is based on memristive devices, which are described by the equations of the redox processes running in the memristive thin metal-oxide-metal films, e.g., Au/ZrO2(Y)/TiN/Ti and Au/SiO2(Y)/TiN/Ti. In order to train the developed ANN to predict epileptiform activity, a supervised learning algorithm was used, which allowed us to adjust the network parameters and train LSTM on the described recordings of neuronal activity. Results After training on the LFP recordings from the hippocampus and medial entorhinal cortex of the mice with chronic epileptiform activity, the proposed deep ANN has demonstrated high values of evaluation metric (root-mean-square error, RMSE) and successfully predicted epileptiform activity shortly before its occurrence (40 ms). The results of the numerical experiments have shown that the RMSE value of 0.019 was reached, which indicates the efficacy of proposed approach. The accuracy of epileptiform activity prediction 40 ms before its occurrence is a significant result and shows the potential of the developed neural network architecture. Conclusion The proposed deep ANN can be used to predict pathological neuronal activity including epileptic seizure (focal) activity in mice before its actual occurrence. Besides, it can be applied for building a long-term prognosis of the disease course based on the LFP data. Thus, the proposed ANN based on memristive devices represents a novel approach to the prediction and analysis of pathological neuronal activity possessing a potential for improving the diagnosis and prognostication of epileptic seizures and other diseases associated with neuronal activity.
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Affiliation(s)
- S.A. Gerasimova
- Researcher, Research Laboratory of Perspective Methods of Multidimensional Data Analysis, Institute of Information Technologies, Mathematics, and Mechanics; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia
| | - A.V. Lebedeva
- Associate Professor, Department of Neurotechnologies, Institute of Biology and Biomedicine; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia
| | - N.V. Gromov
- Laboratory Research Assistant, Research Laboratory of Perspective Methods of Multidimensional Data Analysis, Institute of Information Technologies, Mathematics, and Mechanics; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia
| | - A.E. Malkov
- Senior Researcher, Laboratory of Systemic Organization of Neurons; Institute of Theoretical and Experimental Biophysics of Russian Academy of Sciences, 3 Institutskaya St., Puschino, Moscow Region, 142290, Russia
| | - А.А. Fedulina
- Junior Researcher, Laboratory of Brain Development Genetics, Research Institute of Neurosciences; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia
| | - T.A. Levanova
- Associate Professor, Department of System Dynamics and Control Theory, Institute of Information Technologies, Mathematics, and Mechanics; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia
| | - A.N. Pisarchik
- Head of the Laboratory of Computational Biology, Center for Biomedical Technology; Universidad Politécnica de Madrid, Madrid, 28223, Spain
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11
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Zhou X, Zhao L, Yan C, Zhen W, Lin Y, Li L, Du G, Lu L, Zhang ST, Lu Z, Li D. Thermally stable threshold selector based on CuAg alloy for energy-efficient memory and neuromorphic computing applications. Nat Commun 2023; 14:3285. [PMID: 37280223 DOI: 10.1038/s41467-023-39033-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 05/25/2023] [Indexed: 06/08/2023] Open
Abstract
As a promising candidate for high-density data storage and neuromorphic computing, cross-point memory arrays provide a platform to overcome the von Neumann bottleneck and accelerate neural network computation. In order to suppress the sneak-path current problem that limits their scalability and read accuracy, a two-terminal selector can be integrated at each cross-point to form the one-selector-one-memristor (1S1R) stack. In this work, we demonstrate a CuAg alloy-based, thermally stable and electroforming-free selector device with tunable threshold voltage and over 7 orders of magnitude ON/OFF ratio. A vertically stacked 64 × 64 1S1R cross-point array is further implemented by integrating the selector with SiO2-based memristors. The 1S1R devices exhibit extremely low leakage currents and proper switching characteristics, which are suitable for both storage class memory and synaptic weight storage. Finally, a selector-based leaky integrate-and-fire neuron is designed and experimentally implemented, which expands the application prospect of CuAg alloy selectors from synapses to neurons.
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Affiliation(s)
- Xi Zhou
- The Interdisciplinary Research Center, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 99 Haike Road, Zhangjiang Hi-Tech Park, 201210, Pudong, Shanghai, China
- College of Information Science and Electronic Engineering, Zhejiang University, 38 Zheda Road, 310007, Hangzhou, China
- School of Microelectronics, University of Chinese Academy of Sciences, 19 Yuquan Road, 100049, Beijing, China
| | - Liang Zhao
- College of Information Science and Electronic Engineering, Zhejiang University, 38 Zheda Road, 310007, Hangzhou, China.
- Hefei Reliance Memory Ltd., Bldg. F4-11F, Innovation Industrial Park Phase II, 230088, Hefei, China.
| | - Chu Yan
- College of Information Science and Electronic Engineering, Zhejiang University, 38 Zheda Road, 310007, Hangzhou, China
| | - Weili Zhen
- High Magnetic Field Laboratory, Chinese Academy of Sciences, 230031, Hefei, China
| | - Yinyue Lin
- The Interdisciplinary Research Center, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 99 Haike Road, Zhangjiang Hi-Tech Park, 201210, Pudong, Shanghai, China
- School of Microelectronics, University of Chinese Academy of Sciences, 19 Yuquan Road, 100049, Beijing, China
| | - Le Li
- The Interdisciplinary Research Center, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 99 Haike Road, Zhangjiang Hi-Tech Park, 201210, Pudong, Shanghai, China
- School of Microelectronics, University of Chinese Academy of Sciences, 19 Yuquan Road, 100049, Beijing, China
| | - Guanlin Du
- The Interdisciplinary Research Center, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 99 Haike Road, Zhangjiang Hi-Tech Park, 201210, Pudong, Shanghai, China
- School of Microelectronics, University of Chinese Academy of Sciences, 19 Yuquan Road, 100049, Beijing, China
| | - Linfeng Lu
- The Interdisciplinary Research Center, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 99 Haike Road, Zhangjiang Hi-Tech Park, 201210, Pudong, Shanghai, China
- School of Microelectronics, University of Chinese Academy of Sciences, 19 Yuquan Road, 100049, Beijing, China
| | - Shan-Ting Zhang
- The Interdisciplinary Research Center, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 99 Haike Road, Zhangjiang Hi-Tech Park, 201210, Pudong, Shanghai, China
- School of Microelectronics, University of Chinese Academy of Sciences, 19 Yuquan Road, 100049, Beijing, China
- Zhangjiang Laboratory, 100 Haike Road, Zhangjiang Hi-Tech Park, 201210, Pudong, Shanghai, China
| | - Zhichao Lu
- Hefei Reliance Memory Ltd., Bldg. F4-11F, Innovation Industrial Park Phase II, 230088, Hefei, China
| | - Dongdong Li
- The Interdisciplinary Research Center, Shanghai Advanced Research Institute, Chinese Academy of Sciences, 99 Haike Road, Zhangjiang Hi-Tech Park, 201210, Pudong, Shanghai, China.
- School of Microelectronics, University of Chinese Academy of Sciences, 19 Yuquan Road, 100049, Beijing, China.
- Zhangjiang Laboratory, 100 Haike Road, Zhangjiang Hi-Tech Park, 201210, Pudong, Shanghai, China.
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12
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Liu YH, Wang JJ, Wang HZ, Liu S, Wu YC, Hu SG, Yu Q, Liu Z, Chen TP, Yin Y, Liu Y. Braille recognition by E-skin system based on binary memristive neural network. Sci Rep 2023; 13:5437. [PMID: 37012399 PMCID: PMC10070348 DOI: 10.1038/s41598-023-31934-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 03/20/2023] [Indexed: 04/05/2023] Open
Abstract
Braille system is widely used worldwide for communication by visually impaired people. However, there are still some visually impaired people who are unable to learn Braille system due to various factors, such as the age (too young or too old), brain damage, etc. A wearable and low-cost Braille recognition system may substantially help these people recognize Braille or assist them in Braille learning. In this work, we fabricated polydimethylsiloxane (PDMS)-based flexible pressure sensors to construct an electronic skin (E-skin) for the application of Braille recognition. The E-skin mimics human touch sensing function for collecting Braille information. Braille recognition is realized with a neural network based on memristors. We utilize a binary neural network algorithm with only two bias layers and three fully connected layers. Such neural network design remarkably reduces the calculation burden and, thus, the system cost. Experiments show that the system can achieve a recognition accuracy of up to 91.25%. This work demonstrates the possibility of realizing a wearable and low-cost Braille recognition system and a Braille learning-assistance system.
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Affiliation(s)
- Y H Liu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - J J Wang
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
| | - H Z Wang
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - S Liu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Y C Wu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - S G Hu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Q Yu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Z Liu
- School of Integrated Circuits, Guangdong University of Technology, Guangzhou, 510006, China
| | - T P Chen
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Y Yin
- Graduate School of Engineering, Gunma University, 1-5-1Tenjin, Kiryu, Gunma, 376-8515, Japan
| | - Y Liu
- Deepcreatic Technologies Ltd, Chengdu, 610000, Sichuan, People's Republic of China
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13
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Wang H, Wang J, Hu H, Li G, Hu S, Yu Q, Liu Z, Chen T, Zhou S, Liu Y. Ultra-High-Speed Accelerator Architecture for Convolutional Neural Network Based on Processing-in-Memory Using Resistive Random Access Memory. SENSORS (BASEL, SWITZERLAND) 2023; 23:2401. [PMID: 36904605 PMCID: PMC10007456 DOI: 10.3390/s23052401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 02/16/2023] [Accepted: 02/17/2023] [Indexed: 06/18/2023]
Abstract
Processing-in-Memory (PIM) based on Resistive Random Access Memory (RRAM) is an emerging acceleration architecture for artificial neural networks. This paper proposes an RRAM PIM accelerator architecture that does not use Analog-to-Digital Converters (ADCs) and Digital-to-Analog Converters (DACs). Additionally, no additional memory usage is required to avoid the need for a large amount of data transportation in convolution computation. Partial quantization is introduced to reduce the accuracy loss. The proposed architecture can substantially reduce the overall power consumption and accelerate computation. The simulation results show that the image recognition rate for the Convolutional Neural Network (CNN) algorithm can reach 284 frames per second at 50 MHz using this architecture. The accuracy of the partial quantization remains almost unchanged compared to the algorithm without quantization.
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Affiliation(s)
- Hongzhe Wang
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Junjie Wang
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hao Hu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Guo Li
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shaogang Hu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Qi Yu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhen Liu
- School of Materials and Energy, Guangdong University of Technology, Guangzhou 510006, China
| | - Tupei Chen
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Shijie Zhou
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yang Liu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, China
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14
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Giotis C, Serb A, Manouras V, Stathopoulos S, Prodromakis T. Palimpsest memories stored in memristive synapses. SCIENCE ADVANCES 2022; 8:eabn7920. [PMID: 35731877 PMCID: PMC9217086 DOI: 10.1126/sciadv.abn7920] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 05/09/2022] [Indexed: 06/15/2023]
Abstract
Biological synapses store multiple memories on top of each other in a palimpsest fashion and at different time scales. Palimpsest consolidation is facilitated by the interaction of hidden biochemical processes governing synaptic efficacy during varying lifetimes. This arrangement allows idle memories to be temporarily overwritten without being forgotten, while previously unseen memories are used in the short term. While embedded artificial intelligence can greatly benefit from this functionality, a practical demonstration in hardware is missing. Here, we show how the intrinsic properties of metal-oxide volatile memristors emulate the processes supporting biological palimpsest consolidation. Our memristive synapses exhibit an expanded doubled capacity and protect a consolidated memory while up to hundreds of uncorrelated short-term memories temporarily overwrite it, without requiring specialized instructions. We further demonstrate this technology in the context of visual working memory. This showcases how emerging memory technologies can efficiently expand the capabilities of artificial intelligence hardware toward more generalized learning memories.
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Affiliation(s)
- Christos Giotis
- Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Alexander Serb
- Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
- Centre for Electronics Frontiers, School of Engineering, University of Edinburgh, Edinburgh EH9 3FB, UK
| | - Vasileios Manouras
- Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Spyros Stathopoulos
- Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
| | - Themis Prodromakis
- Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK
- Centre for Electronics Frontiers, School of Engineering, University of Edinburgh, Edinburgh EH9 3FB, UK
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15
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Spatial-temporal dynamics of a non-monotone reaction-diffusion Hopfield’s neural network model with delays. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07036-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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16
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Li M, Hong Q, Wang X. Memristor-based circuit implementation of Competitive Neural Network based on online unsupervised Hebbian learning rule for pattern recognition. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06361-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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17
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Abstract
With the development of the Internet of things, artificial intelligence, and wearable devices, massive amounts of data are generated and need to be processed. High standards are required to store and analyze this information. In the face of the explosive growth of information, the memory used in data storage and processing faces great challenges. Among many types of memories, memristors have received extensive attentions due to their low energy consumption, strong tolerance, simple structure, and strong miniaturization. However, they still face many problems, especially in the application of artificial bionic synapses, which call for higher requirements in the mechanical properties of the device. The progress of integrated circuit and micro-processing manufacturing technology has greatly promoted development of the flexible memristor. The use of a flexible memristor to simulate nerve synapses will provide new methods for neural network computing and bionic sensing systems. In this paper, the materials and structure of the flexible memristor are summarized and discussed, and the latest configuration and new materials are described. In addition, this paper will focus on its application in artificial bionic synapses and discuss the challenges and development direction of flexible memristors from this perspective.
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18
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Shi J, Zeng Z. Design of In-Situ Learning Bidirectional Associative Memory Neural Network Circuit With Memristor Synapse. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2021. [DOI: 10.1109/tetci.2020.3005703] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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19
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Fahimi Z, Mahmoodi MR, Nili H, Polishchuk V, Strukov DB. Combinatorial optimization by weight annealing in memristive hopfield networks. Sci Rep 2021; 11:16383. [PMID: 34385475 PMCID: PMC8361025 DOI: 10.1038/s41598-020-78944-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 11/17/2020] [Indexed: 11/16/2022] Open
Abstract
The increasing utility of specialized circuits and growing applications of optimization call for the development of efficient hardware accelerator for solving optimization problems. Hopfield neural network is a promising approach for solving combinatorial optimization problems due to the recent demonstrations of efficient mixed-signal implementation based on emerging non-volatile memory devices. Such mixed-signal accelerators also enable very efficient implementation of various annealing techniques, which are essential for finding optimal solutions. Here we propose a "weight annealing" approach, whose main idea is to ease convergence to the global minima by keeping the network close to its ground state. This is achieved by initially setting all synaptic weights to zero, thus ensuring a quick transition of the Hopfield network to its trivial global minima state and then gradually introducing weights during the annealing process. The extensive numerical simulations show that our approach leads to a better, on average, solutions for several representative combinatorial problems compared to prior Hopfield neural network solvers with chaotic or stochastic annealing. As a proof of concept, a 13-node graph partitioning problem and a 7-node maximum-weight independent set problem are solved experimentally using mixed-signal circuits based on, correspondingly, a 20 × 20 analog-grade TiO2 memristive crossbar and a 12 × 10 eFlash memory array.
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Affiliation(s)
- Z Fahimi
- UC Santa Barbara, Santa Barbara, CA, 93106-9560, USA.
| | - M R Mahmoodi
- UC Santa Barbara, Santa Barbara, CA, 93106-9560, USA.
| | - H Nili
- UC Santa Barbara, Santa Barbara, CA, 93106-9560, USA
| | | | - D B Strukov
- UC Santa Barbara, Santa Barbara, CA, 93106-9560, USA
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20
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Sheng Y, Huang T, Zeng Z, Miao X. Global Exponential Stability of Memristive Neural Networks With Mixed Time-Varying Delays. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:3690-3699. [PMID: 32857700 DOI: 10.1109/tnnls.2020.3015944] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This article investigates the Lagrange exponential stability and the Lyapunov exponential stability of memristive neural networks with discrete and distributed time-varying delays (DMNNs). By means of inequality techniques, theories of the M-matrix, and the comparison strategy, the Lagrange exponential stability of the underlying DMNNs is considered in the sense of Filippov, and the globally exponentially attractive set is estimated through employing the M-matrix and external input. Especially, when the external input is not concerned, the Lyapunov exponential stability of the corresponding DMNNs is developed immediately in the form of an M-matrix, which contains some published outcomes as special cases. Furthermore, by constructing an M-matrix-based differential system, the Lyapunov exponential stability of the DMNNs is studied, which is less conservative than some existing ones. Finally, three simulation examples are carried out to examine the validness of the theories.
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21
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Shamsi J, Avedillo MJ, Linares-Barranco B, Serrano-Gotarredona T. Hardware Implementation of Differential Oscillatory Neural Networks Using VO 2-Based Oscillators and Memristor-Bridge Circuits. Front Neurosci 2021; 15:674567. [PMID: 34335158 PMCID: PMC8322448 DOI: 10.3389/fnins.2021.674567] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 06/15/2021] [Indexed: 11/13/2022] Open
Abstract
Oscillatory Neural Networks (ONNs) are currently arousing interest in the research community for their potential to implement very fast, ultra-low-power computing tasks by exploiting specific emerging technologies. From the architectural point of view, ONNs are based on the synchronization of oscillatory neurons in cognitive processing, as occurs in the human brain. As emerging technologies, VO2 and memristive devices show promising potential for the efficient implementation of ONNs. Abundant literature is now becoming available pertaining to the study and building of ONNs based on VO2 devices and resistive coupling, such as memristors. One drawback of direct resistive coupling is that physical resistances cannot be negative, but from the architectural and computational perspective this would be a powerful advantage when interconnecting weights in ONNs. Here we solve the problem by proposing a hardware implementation technique based on differential oscillatory neurons for ONNs (DONNs) with VO2-based oscillators and memristor-bridge circuits. Each differential oscillatory neuron is made of a pair of VO2 oscillators operating in anti-phase. This way, the neurons provide a pair of differential output signals in opposite phase. The memristor-bridge circuit is used as an adjustable coupling function that is compatible with differential structures and capable of providing both positive and negative weights. By combining differential oscillatory neurons and memristor-bridge circuits, we propose the hardware implementation of a fully connected differential ONN (DONN) and use it as an associative memory. The standard Hebbian rule is used for training, and the weights are then mapped to the memristor-bridge circuit through a proposed mapping rule. The paper also introduces some functional and hardware specifications to evaluate the design. Evaluation is performed by circuit-level electrical simulations and shows that the retrieval accuracy of the proposed design is comparable to that of classic Hopfield Neural Networks.
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Affiliation(s)
- Jafar Shamsi
- Instituto de Microelectrónica de Sevilla (CSIC), Universidad of Sevilla, Seville, Spain
| | - María José Avedillo
- Instituto de Microelectrónica de Sevilla (CSIC), Universidad of Sevilla, Seville, Spain
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22
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Wang J, Zhuge X, Zhuge F. Hybrid oxide brain-inspired neuromorphic devices for hardware implementation of artificial intelligence. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2021; 22:326-344. [PMID: 34025215 PMCID: PMC8128179 DOI: 10.1080/14686996.2021.1911277] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
The state-of-the-art artificial intelligence technologies mainly rely on deep learning algorithms based on conventional computers with classical von Neumann computing architectures, where the memory and processing units are separated resulting in an enormous amount of energy and time consumed in the data transfer process. Inspired by the human brain acting like an ultra-highly efficient biological computer, neuromorphic computing is proposed as a technology for hardware implementation of artificial intelligence. Artificial synapses are the main component of a neuromorphic computing architecture. Memristors are considered to be a relatively ideal candidate for artificial synapse applications due to their high scalability and low power consumption. Oxides are most widely used in memristors due to the ease of fabrication and high compatibility with complementary metal-oxide-semiconductor processes. However, oxide memristors suffer from unsatisfactory stability and reliability. Oxide-based hybrid structures can effectively improve the device stability and reliability, therefore providing a promising prospect for the application of oxide memristors to neuromorphic computing. This work reviews the recent advances in the development of hybrid oxide memristive synapses. The discussion is organized according to the blending schemes as well as the working mechanisms of hybrid oxide memristors.
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Affiliation(s)
- Jingrui Wang
- School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo, China
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Xia Zhuge
- School of Electronic and Information Engineering, Ningbo University of Technology, Ningbo, China
| | - Fei Zhuge
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- CONTACT Fei Zhuge Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo315201, China
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23
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Chowdhury S, Hill HM, Rigosi AF, Briggs A, Berger H, Newell DB, Walker ARH, Tavazza F. Examining Experimental Raman Mode Behavior in Mono- and Bilayer 2H-TaSe 2 via Density Functional Theory: Implications for Quantum Information Science. ACS APPLIED NANO MATERIALS 2021; 4:10.1021/acsanm.0c03222. [PMID: 34250452 PMCID: PMC8268966 DOI: 10.1021/acsanm.0c03222] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Tantalum diselenide (TaSe2) is a metallic transition metal dichalcogenide whose structure and vibrational behavior strongly depend on temperature and thickness, and this behavior includes the emergence of charge density wave (CDW) states at very low temperatures. In this work, observed Raman modes for mono- and bilayer are described across several spectral regions and compared to those seen in the bulk case. These modes, which include an experimentally observed forbidden Raman mode and low-frequency CDWs, are then matched to corresponding vibrations predicted by density functional theory (DFT). The reported match between experimental and computational results supports the presented vibrational visualizations of these modes. Support is also provided by experimental phonons observed in additional Raman spectra as a function of temperature and thickness. These results highlight the importance of understanding CDWs since they are likely to play a fundamental role in the future realization of solid-state quantum information platforms based on nonequilibrium phenomena.
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Affiliation(s)
- Sugata Chowdhury
- National Institute of Standards and Technology (NIST), Gaithersburg, Maryland 20899, United States; Department of Physics and Astronomy, Howard University, Washington, D.C. 20059, United States
| | - Heather M Hill
- National Institute of Standards and Technology (NIST), Gaithersburg, Maryland 20899, United States
| | - Albert F Rigosi
- National Institute of Standards and Technology (NIST), Gaithersburg, Maryland 20899, United States
| | - Andrew Briggs
- National Institute of Standards and, Technology (NIST), Gaithersburg, Maryland 20899, United States
| | - Helmuth Berger
- École Polytechnique Fédérale de Lausanne (EPFL), Institut de Physique des Nanostructures, CH-1015 Lausanne, Switzerland
| | - David B Newell
- National Institute of Standards and Technology (NIST), Gaithersburg, Maryland 20899, United States
| | - Angela R Hight Walker
- National Institute of Standards and Technology (NIST), Gaithersburg, Maryland 20899, United States
| | - Francesca Tavazza
- National Institute of Standards and Technology (NIST), Gaithersburg, Maryland 20899, United States
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24
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Guo T, Sun B, Ranjan S, Jiao Y, Wei L, Zhou YN, Wu YA. From Memristive Materials to Neural Networks. ACS APPLIED MATERIALS & INTERFACES 2020; 12:54243-54265. [PMID: 33232112 DOI: 10.1021/acsami.0c10796] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The information technologies have been increasing exponentially following Moore's law over the past decades. This has fundamentally changed the ways of work and life. However, further improving data process efficiency is facing great challenges because of physical and architectural limitations. More powerful computational methodologies are crucial to fulfill the technology gap in the post-Moore's law period. The memristor exhibits promising prospects in information storage, high-performance computing, and artificial intelligence. Since the memristor was theoretically predicted by L. O. Chua in 1971 and experimentally confirmed by HP Laboratories in 2008, it has attracted great attention from worldwide researchers. The intrinsic properties of memristors, such as simple structure, low power consumption, compatibility with the complementary metal oxide-semiconductor (CMOS) process, and dual functionalities of the data storage and computation, demonstrate great prospects in many applications. In this review, we cover the memristor-relevant computing technologies, from basic materials to in-memory computing and future prospects. First, the materials and mechanisms in the memristor are discussed. Then, we present the development of the memristor in the domains of the synapse simulating, in-memory logic computing, deep neural networks (DNNs) and spiking neural networks (SNNs). Finally, the existent technology challenges and outlook of the state-of-art applications are proposed.
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Affiliation(s)
- Tao Guo
- Department of Mechanical and Mechatronics Engineering, Waterloo Institute of Nanotechnology, Centre for Advanced Materials Joining, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
| | - Bai Sun
- Department of Mechanical and Mechatronics Engineering, Waterloo Institute of Nanotechnology, Centre for Advanced Materials Joining, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials (Ministry of Education of China), Southwest Jiaotong University, Chengdu, Sichuan 610031, China
| | - Shubham Ranjan
- Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
| | - Yixuan Jiao
- Department of Mechanical and Mechatronics Engineering, Waterloo Institute of Nanotechnology, Centre for Advanced Materials Joining, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
- Department of Chemical Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
| | - Lan Wei
- Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
| | - Y Norman Zhou
- Department of Mechanical and Mechatronics Engineering, Waterloo Institute of Nanotechnology, Centre for Advanced Materials Joining, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
| | - Yimin A Wu
- Department of Mechanical and Mechatronics Engineering, Waterloo Institute of Nanotechnology, Centre for Advanced Materials Joining, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
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25
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Saito K, Aono M, Kasai S. Amoeba-inspired analog electronic computing system integrating resistance crossbar for solving the travelling salesman problem. Sci Rep 2020; 10:20772. [PMID: 33247175 PMCID: PMC7695837 DOI: 10.1038/s41598-020-77617-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 11/13/2020] [Indexed: 11/09/2022] Open
Abstract
Combinatorial optimization to search for the best solution across a vast number of legal candidates requires the development of a domain-specific computing architecture that can exploit the computational power of physical processes, as conventional general-purpose computers are not powerful enough. Recently, Ising machines that execute quantum annealing or related mechanisms for rapid search have attracted attention. These machines, however, are hard to map application problems into their architecture, and often converge even at an illegal candidate. Here, we demonstrate an analogue electronic computing system for solving the travelling salesman problem, which mimics efficient foraging behaviour of an amoeboid organism by the spontaneous dynamics of an electric current in its core and enables a high problem-mapping flexibility and resilience using a resistance crossbar circuit. The system has high application potential, as it can determine a high-quality legal solution in a time that grows proportionally to the problem size without suffering from the weaknesses of Ising machines.
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Affiliation(s)
- Kenta Saito
- Research Center for Integrated Quantum Electronics and Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan.
| | - Masashi Aono
- Amoeba Energy Co., Ltd., Fujisawa, Japan.,Graduate School of Media and Governance, Keio University, Fujisawa, Japan.,Graduate School of Science and Technology, Keio University, Yokohama, Japan
| | - Seiya Kasai
- Research Center for Integrated Quantum Electronics and Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan. .,Center for Human Nature, Artificial Intelligence, and Neuroscience, Hokkaido University, Sapporo, Japan.
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Yang L, Zeng Z, Huang Y. An Associative-Memory-Based Reconfigurable Memristive Neuromorphic System With Synchronous Weight Training. IEEE Trans Cogn Dev Syst 2020. [DOI: 10.1109/tcds.2019.2932179] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Yang K, Duan Q, Wang Y, Zhang T, Yang Y, Huang R. Transiently chaotic simulated annealing based on intrinsic nonlinearity of memristors for efficient solution of optimization problems. SCIENCE ADVANCES 2020; 6:eaba9901. [PMID: 32851168 PMCID: PMC7428342 DOI: 10.1126/sciadv.aba9901] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 07/01/2020] [Indexed: 05/04/2023]
Abstract
Optimization problems are ubiquitous in scientific research, engineering, and daily lives. However, solving a complex optimization problem often requires excessive computing resource and time and faces challenges in easily getting trapped into local optima. Here, we propose a memristive optimizer hardware based on a Hopfield network, which introduces transient chaos to simulated annealing in aid of jumping out of the local optima while ensuring convergence. A single memristor crossbar is used to store the weight parameters of a fully connected Hopfield network and adjust the network dynamics in situ. Furthermore, we harness the intrinsic nonlinearity of memristors within the crossbar to implement an efficient and simplified annealing process for the optimization. Solutions of continuous function optimizations on sphere function and Matyas function as well as combinatorial optimization on Max-cut problem are experimentally demonstrated, indicating great potential of the transiently chaotic memristive network in solving optimization problems in general.
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Affiliation(s)
- Ke Yang
- Key Laboratory of Microelectronic Devices and Circuits (MOE), Department of Micro/nanoelectronics, Peking University, Beijing 100871, China
| | - Qingxi Duan
- Key Laboratory of Microelectronic Devices and Circuits (MOE), Department of Micro/nanoelectronics, Peking University, Beijing 100871, China
| | - Yanghao Wang
- Key Laboratory of Microelectronic Devices and Circuits (MOE), Department of Micro/nanoelectronics, Peking University, Beijing 100871, China
| | - Teng Zhang
- Key Laboratory of Microelectronic Devices and Circuits (MOE), Department of Micro/nanoelectronics, Peking University, Beijing 100871, China
| | - Yuchao Yang
- Key Laboratory of Microelectronic Devices and Circuits (MOE), Department of Micro/nanoelectronics, Peking University, Beijing 100871, China
- Center for Brain Inspired Chips, Institute for Artificial Intelligence, Peking University, Beijing 100871, China
- Frontiers Science Center for Nano-optoelectronics, Peking University, Beijing 100871, China
- Corresponding author. (Y.Y.); (R.H.)
| | - Ru Huang
- Key Laboratory of Microelectronic Devices and Circuits (MOE), Department of Micro/nanoelectronics, Peking University, Beijing 100871, China
- Center for Brain Inspired Chips, Institute for Artificial Intelligence, Peking University, Beijing 100871, China
- Frontiers Science Center for Nano-optoelectronics, Peking University, Beijing 100871, China
- Corresponding author. (Y.Y.); (R.H.)
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Design of Hopfield network for cryptographic application by spintronic memristors. Neural Comput Appl 2020. [DOI: 10.1007/s00521-019-04454-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Ballarini D, Gianfrate A, Panico R, Opala A, Ghosh S, Dominici L, Ardizzone V, De Giorgi M, Lerario G, Gigli G, Liew TCH, Matuszewski M, Sanvitto D. Polaritonic Neuromorphic Computing Outperforms Linear Classifiers. NANO LETTERS 2020; 20:3506-3512. [PMID: 32251601 DOI: 10.1021/acs.nanolett.0c00435] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Machine learning software applications are ubiquitous in many fields of science and society for their outstanding capability to solve computationally vast problems like the recognition of patterns and regularities in big data sets. In spite of these impressive achievements, such processors are still based on the so-called von Neumann architecture, which is a bottleneck for faster and power-efficient neuromorphic computation. Therefore, one of the main goals of research is to conceive physical realizations of artificial neural networks capable of performing fully parallel and ultrafast operations. Here we show that lattices of exciton-polariton condensates accomplish neuromorphic computing with outstanding accuracy thanks to their high optical nonlinearity. We demonstrate that our neural network significantly increases the recognition efficiency compared with the linear classification algorithms on one of the most widely used benchmarks, the MNIST problem, showing a concrete advantage from the integration of optical systems in neural network architectures.
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Affiliation(s)
- Dario Ballarini
- CNR NANOTEC-Institute of Nanotechnology, Via Monteroni, 73100 Lecce, Italy
| | - Antonio Gianfrate
- CNR NANOTEC-Institute of Nanotechnology, Via Monteroni, 73100 Lecce, Italy
| | - Riccardo Panico
- CNR NANOTEC-Institute of Nanotechnology, Via Monteroni, 73100 Lecce, Italy
| | - Andrzej Opala
- Institute of Physics, Polish Academy of Sciences, Al. Lotników 32/46, PL-02-668 Warsaw, Poland
| | - Sanjib Ghosh
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371
| | - Lorenzo Dominici
- CNR NANOTEC-Institute of Nanotechnology, Via Monteroni, 73100 Lecce, Italy
| | - Vincenzo Ardizzone
- CNR NANOTEC-Institute of Nanotechnology, Via Monteroni, 73100 Lecce, Italy
| | - Milena De Giorgi
- CNR NANOTEC-Institute of Nanotechnology, Via Monteroni, 73100 Lecce, Italy
| | - Giovanni Lerario
- CNR NANOTEC-Institute of Nanotechnology, Via Monteroni, 73100 Lecce, Italy
| | - Giuseppe Gigli
- CNR NANOTEC-Institute of Nanotechnology, Via Monteroni, 73100 Lecce, Italy
| | - Timothy C H Liew
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371
| | - Michal Matuszewski
- Institute of Physics, Polish Academy of Sciences, Al. Lotników 32/46, PL-02-668 Warsaw, Poland
| | - Daniele Sanvitto
- CNR NANOTEC-Institute of Nanotechnology, Via Monteroni, 73100 Lecce, Italy
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Shirai S, Acharya SK, Bose SK, Mallinson JB, Galli E, Pike MD, Arnold MD, Brown SA. Long-range temporal correlations in scale-free neuromorphic networks. Netw Neurosci 2020; 4:432-447. [PMID: 32537535 PMCID: PMC7286302 DOI: 10.1162/netn_a_00128] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 01/17/2020] [Indexed: 12/05/2022] Open
Abstract
Biological neuronal networks are the computing engines of the mammalian brain. These networks exhibit structural characteristics such as hierarchical architectures, small-world attributes, and scale-free topologies, providing the basis for the emergence of rich temporal characteristics such as scale-free dynamics and long-range temporal correlations. Devices that have both the topological and the temporal features of a neuronal network would be a significant step toward constructing a neuromorphic system that can emulate the computational ability and energy efficiency of the human brain. Here we use numerical simulations to show that percolating networks of nanoparticles exhibit structural properties that are reminiscent of biological neuronal networks, and then show experimentally that stimulation of percolating networks by an external voltage stimulus produces temporal dynamics that are self-similar, follow power-law scaling, and exhibit long-range temporal correlations. These results are expected to have important implications for the development of neuromorphic devices, especially for those based on the concept of reservoir computing. Biological neuronal networks exhibit well-defined properties such as hierarchical structures and scale-free topologies, as well as a high degree of local clustering and short path lengths between nodes. These structural properties are intimately connected to the observed long-range temporal correlations in the network dynamics. Fabrication of artificial networks with similar structural properties would facilitate brain-like (“neuromorphic”) computing. Here we show experimentally that percolating networks of nanoparticles exhibit similar long-range temporal correlations to those of biological neuronal networks and use simulations to demonstrate that the dynamics arise from an underlying scale-free network architecture. We discuss similarities between the biological and percolating systems and highlight the potential for the percolating networks to be used in neuromorphic computing applications.
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Affiliation(s)
- Shota Shirai
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matū, University of Canterbury, Christchurch, New Zealand
| | - Susant Kumar Acharya
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matū, University of Canterbury, Christchurch, New Zealand
| | - Saurabh Kumar Bose
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matū, University of Canterbury, Christchurch, New Zealand
| | - Joshua Brian Mallinson
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matū, University of Canterbury, Christchurch, New Zealand
| | - Edoardo Galli
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matū, University of Canterbury, Christchurch, New Zealand
| | - Matthew D Pike
- Electrical and Electronics Engineering, University of Canterbury, Christchurch, New Zealand
| | - Matthew D Arnold
- School of Mathematical and Physical Sciences, University of Technology Sydney, Australia
| | - Simon Anthony Brown
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matū, University of Canterbury, Christchurch, New Zealand
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Chen T, Wang L, Duan S. Implementation of circuit for reconfigurable memristive chaotic neural network and its application in associative memory. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.10.100] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Nopoles G, Vanhoenshoven F, Falcon R, Vanhoof K. Nonsynaptic Error Backpropagation in Long-Term Cognitive Networks. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:865-875. [PMID: 31059456 DOI: 10.1109/tnnls.2019.2910555] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
We introduce a neural cognitive mapping technique named long-term cognitive network (LTCN) that is able to memorize long-term dependencies between a sequence of input and output vectors, especially in those scenarios that require predicting the values of multiple dependent variables at the same time. The proposed technique is an extension of a recently proposed method named short-term cognitive network that aims at preserving the expert knowledge encoded in the weight matrix while optimizing the nonlinear mappings provided by the transfer function of each neuron. A nonsynaptic, backpropagation-based learning algorithm powered by stochastic gradient descent is put forward to iteratively optimize four parameters of the generalized sigmoid transfer function associated with each neuron. Numerical simulations over 35 multivariate regression and pattern completion data sets confirm that the proposed LTCN algorithm attains statistically significant performance differences with respect to other well-known state-of-the-art methods.
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Sun Y, Wen D, Xie Y, Sun F, Mo X, Zhu J, Sun H. Logic Gate Functions Built with Nonvolatile Resistive Switching and Thermoresponsive Memory Based on Biologic Proteins. J Phys Chem Lett 2019; 10:7745-7752. [PMID: 31773960 DOI: 10.1021/acs.jpclett.9b03238] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Logic gate functions built with nonvolatile resistive switching and thermoresponsive memory based on biologic proteins were investigated. The "NAND" and "NOR" functions of logic gates in soya protein devices have been built at room temperature by their nonvolatile ternary WORM resistive switching behaviors. Furthermore, heating the devices from room temperature to 358 K results in a switch from tristable state to bistable state WORM resistive switching behavior, indicating that the thermoresponsiveness can be efficiently memorized. The biologic transient nonvolatile memory device consisting of soya protein is illustrated. This device exhibits a long data retention time (104 s) and significant HRS/LRS ratio (∼105); the transient response of the current to voltage of an as-fabricated device is also explored. The soya protein based memory device on a gelatin film substrate is also assessed to validate the feasibility of degradation and biological compatibility for the implantable biological electronic device, that is, innoxious and avirulent to the human body. This can offer alternative avenues for exploring prospective bioelectronic devices.
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Affiliation(s)
- Yanmei Sun
- HLJ Province Key Laboratories of Senior-Education for Electronic Engineering , Heilongjiang University , Harbin 150080 , China
- School of Electronic Engineering , Heilongjiang University , Harbin 150080 , China
| | - Dianzhong Wen
- HLJ Province Key Laboratories of Senior-Education for Electronic Engineering , Heilongjiang University , Harbin 150080 , China
- School of Electronic Engineering , Heilongjiang University , Harbin 150080 , China
| | - Yaqin Xie
- HLJ Province Key Laboratories of Senior-Education for Electronic Engineering , Heilongjiang University , Harbin 150080 , China
- School of Electronic Engineering , Heilongjiang University , Harbin 150080 , China
| | - Fengyun Sun
- HLJ Province Key Laboratories of Senior-Education for Electronic Engineering , Heilongjiang University , Harbin 150080 , China
- School of Electronic Engineering , Heilongjiang University , Harbin 150080 , China
| | - Xichao Mo
- HLJ Province Key Laboratories of Senior-Education for Electronic Engineering , Heilongjiang University , Harbin 150080 , China
- School of Electronic Engineering , Heilongjiang University , Harbin 150080 , China
| | - Jingyuan Zhu
- HLJ Province Key Laboratories of Senior-Education for Electronic Engineering , Heilongjiang University , Harbin 150080 , China
- School of Electronic Engineering , Heilongjiang University , Harbin 150080 , China
| | - He Sun
- HLJ Province Key Laboratories of Senior-Education for Electronic Engineering , Heilongjiang University , Harbin 150080 , China
- School of Electronic Engineering , Heilongjiang University , Harbin 150080 , China
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Caravelli F. Asymptotic Behavior of Memristive Circuits. ENTROPY 2019; 21:e21080789. [PMID: 33267502 PMCID: PMC7515318 DOI: 10.3390/e21080789] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 08/02/2019] [Accepted: 08/06/2019] [Indexed: 11/16/2022]
Abstract
The interest in memristors has risen due to their possible application both as memory units and as computational devices in combination with CMOS. This is in part due to their nonlinear dynamics, and a strong dependence on the circuit topology. We provide evidence that also purely memristive circuits can be employed for computational purposes. In the present paper we show that a polynomial Lyapunov function in the memory parameters exists for the case of DC controlled memristors. Such a Lyapunov function can be asymptotically approximated with binary variables, and mapped to quadratic combinatorial optimization problems. This also shows a direct parallel between memristive circuits and the Hopfield-Little model. In the case of Erdos-Renyi random circuits, we show numerically that the distribution of the matrix elements of the projectors can be roughly approximated with a Gaussian distribution, and that it scales with the inverse square root of the number of elements. This provides an approximated but direct connection with the physics of disordered system and, in particular, of mean field spin glasses. Using this and the fact that the interaction is controlled by a projector operator on the loop space of the circuit. We estimate the number of stationary points of the approximate Lyapunov function and provide a scaling formula as an upper bound in terms of the circuit topology only.
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Affiliation(s)
- Francesco Caravelli
- Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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36
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Hong Q, Li Y, Wang X. Memristive continuous Hopfield neural network circuit for image restoration. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04305-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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37
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Pervaiz AZ, Sutton BM, Ghantasala LA, Camsari KY. Weighted p -Bits for FPGA Implementation of Probabilistic Circuits. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019; 30:1920-1926. [PMID: 30387748 DOI: 10.1109/tnnls.2018.2874565] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Probabilistic spin logic is a recently proposed computing paradigm based on unstable stochastic units called probabilistic bits ( p -bits) that can be correlated to form probabilistic circuits (p-circuits). These p-circuits can be used to solve the problems of optimization, inference, and implement precise Boolean functions in an "inverted" mode, where a given Boolean circuit can operate in reverse to find the input combinations that are consistent with a given output. In this brief, we present a scalable field-programmable gate array implementation of such invertible p-circuits. We implement a "weighted" p -bit that combines stochastic units with localized memory structures. We also present a generalized tile of weighted p -bits to which a large class of problems beyond invertible Boolean logic can be mapped and how invertibility can be applied to interesting problems such as the NP-complete subset sum problem by solving a small instance of this problem in hardware.
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Sheng Y, Lewis FL, Zeng Z. Exponential Stabilization of Fuzzy Memristive Neural Networks With Hybrid Unbounded Time-Varying Delays. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019; 30:739-750. [PMID: 30047913 DOI: 10.1109/tnnls.2018.2852497] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
This paper is concerned with exponential stabilization for a class of Takagi-Sugeno fuzzy memristive neural networks (FMNNs) with unbounded discrete and distributed time-varying delays. Under the framework of Filippov solutions, algebraic criteria are established to guarantee exponential stabilization of the addressed FMNNs with hybrid unbounded time delays via designing a fuzzy state feedback controller by exploiting inequality techniques, calculus theorems, and theories of fuzzy sets. The obtained results in this paper enhance and generalize some existing ones. Meanwhile, a general theoretical framework is proposed to investigate the dynamical behaviors of various neural networks with mixed infinite time delays. Finally, two simulation examples are performed to illustrate the validity of the derived outcomes.
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Liang H, Cheng H, Wei J, Zhang L, Yang L, Zhao Y, Guo H. Memristive Neural Networks: A Neuromorphic Paradigm for Extreme Learning Machine. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2019. [DOI: 10.1109/tetci.2018.2849721] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Liu J, Gong M, He H. Deep associative neural network for associative memory based on unsupervised representation learning. Neural Netw 2019; 113:41-53. [PMID: 30780044 DOI: 10.1016/j.neunet.2019.01.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 10/31/2018] [Accepted: 01/20/2019] [Indexed: 01/04/2023]
Abstract
This paper presents a deep associative neural network (DANN) based on unsupervised representation learning for associative memory. In brain, the knowledge is learnt by associating different types of sensory data, such as image and voice. The associative memory models which imitate such a learning process have been studied for decades but with simpler architectures they fail to deal with large scale complex data as compared with deep neural networks. Therefore, we define a deep architecture consisting of a perception layer and hierarchical propagation layers. To learn the network parameters, we define a probabilistic model for the whole network inspired from unsupervised representation learning models. The model is optimized by a modified contrastive divergence algorithm with a novel iterated sampling process. After training, given a new data or corrupted data, the correct label or corrupted part is associated by the network. The DANN is able to achieve many machine learning problems, including not only classification, but also depicting the data given a label and recovering corrupted images. Experiments on MNIST digits and CIFAR-10 datasets demonstrate the learning capability of the proposed DANN.
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Affiliation(s)
- Jia Liu
- School of Electronic Engineering, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, Shaanxi Province 710071, China
| | - Maoguo Gong
- School of Electronic Engineering, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, Shaanxi Province 710071, China
| | - Haibo He
- Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA.
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Fu Q, Cai J, Zhong S, Yu Y, Shan Y. Input-to-state stability of discrete-time memristive neural networks with two delay components. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2018.10.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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42
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Huang HM, Yang R, Tan ZH, He HK, Zhou W, Xiong J, Guo X. Quasi-Hodgkin-Huxley Neurons with Leaky Integrate-and-Fire Functions Physically Realized with Memristive Devices. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2019; 31:e1803849. [PMID: 30461092 DOI: 10.1002/adma.201803849] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 10/25/2018] [Indexed: 06/09/2023]
Abstract
Artificial neurons with functions such as leaky integrate-and-fire (LIF) and spike output are essential for brain-inspired computation with high efficiency. However, previously implemented artificial neurons, e.g., Hodgkin-Huxley (HH) neurons, integrate-and-fire (IF) neurons, and LIF neurons, only achieve partial functionality of a biological neuron. In this work, quasi-HH neurons with leaky integrate-and-fire functions are physically demonstrated with a volatile memristive device, W/WO3 /poly(3,4-ethylenedioxythiophene): polystyrene sulfonate/Pt. The resistive switching behavior of the device can be attributed to the migration of protons, unlike the migration of oxygen ions normally involved in oxide-based memristors. With multifunctions similar to their biological counterparts, quasi-HH neurons are advantageous over the reported HH and LIF neurons, demonstrating their potential for neuromorphic computing applications.
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Affiliation(s)
- He-Ming Huang
- Laboratory of Solid State Ionics, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Rui Yang
- Laboratory of Solid State Ionics, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Zheng-Hua Tan
- Laboratory of Solid State Ionics, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Hui-Kai He
- Laboratory of Solid State Ionics, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Wen Zhou
- Laboratory of Solid State Ionics, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Jue Xiong
- Laboratory of Solid State Ionics, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Xin Guo
- Laboratory of Solid State Ionics, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
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Wang S, Hou X, Liu L, Li J, Shan Y, Wu S, Zhang DW, Zhou P. A Photoelectric-Stimulated MoS 2 Transistor for Neuromorphic Engineering. RESEARCH (WASHINGTON, D.C.) 2019; 2019:1618798. [PMID: 31922128 PMCID: PMC6946262 DOI: 10.34133/2019/1618798] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2019] [Accepted: 10/14/2019] [Indexed: 12/19/2022]
Abstract
The von Neumann bottleneck has spawned the rapid expansion of neuromorphic engineering and brain-like networks. Synapses serve as bridges for information transmission and connection in the biological nervous system. The direct implementation of neural networks may depend on novel materials and devices that mimic natural neuronal and synaptic behavior. By exploiting the interfacial effects between MoS2 and AlOx, we demonstrate that an h-BN-encapsulated MoS2 artificial synapse transistor can mimic the basic synaptic behaviors, including EPSC, PPF, LTP, and LTD. Efficient optoelectronic spikes enable simulation of synaptic gain, frequency, and weight plasticity. The Pavlov classical conditioning experiment was successfully simulated by electrical tuning, showing associated learning behavior. In addition, h-BN encapsulation effectively improves the environmental time stability of our devices. Our h-BN-encapsulated MoS2 artificial synapse provides a new paradigm for hardware implementation of neuromorphic engineering.
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Affiliation(s)
- Shuiyuan Wang
- ASIC & System State Key Lab., School of Microelectronics, Fudan University, Shanghai 200433, China
| | - Xiang Hou
- ASIC & System State Key Lab., School of Microelectronics, Fudan University, Shanghai 200433, China
| | - Lan Liu
- ASIC & System State Key Lab., School of Microelectronics, Fudan University, Shanghai 200433, China
| | - Jingyu Li
- ASIC & System State Key Lab., School of Microelectronics, Fudan University, Shanghai 200433, China
| | - Yuwei Shan
- Department of Physics, State Key Laboratory of Surface Physics, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), and Institute for Nanoelectronic Devices and Quantum Computing, Fudan University, Shanghai 200433, China
| | - Shiwei Wu
- Department of Physics, State Key Laboratory of Surface Physics, Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), and Institute for Nanoelectronic Devices and Quantum Computing, Fudan University, Shanghai 200433, China
| | - David Wei Zhang
- ASIC & System State Key Lab., School of Microelectronics, Fudan University, Shanghai 200433, China
| | - Peng Zhou
- ASIC & System State Key Lab., School of Microelectronics, Fudan University, Shanghai 200433, China
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Abstract
We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.
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45
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Yang L, Zeng Z, Huang Y, Wen S. Memristor-Based Circuit Implementations of Recognition Network and Recall Network With Forgetting Stages. IEEE Trans Cogn Dev Syst 2018. [DOI: 10.1109/tcds.2018.2859303] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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46
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Mikhaylov AN, Pigareva YI, Pimashkin AS, Lobov SA, Kazantsev VB, Morozov OA, Ovchinnikov PE, Antonov IN, Belov AI, Korolev DS, Sharapov AN, Gryaznov EG, Gorshkov ON. One-Board Design and Simulation of Double-Layer Perceptron Based on Metal-Oxide Memristive Nanostructures. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2018. [DOI: 10.1109/tetci.2018.2829922] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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47
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Wang JJ, Hu SG, Zhan XT, Yu Q, Liu Z, Chen TP, Yin Y, Hosaka S, Liu Y. Handwritten-Digit Recognition by Hybrid Convolutional Neural Network based on HfO 2 Memristive Spiking-Neuron. Sci Rep 2018; 8:12546. [PMID: 30135449 PMCID: PMC6105732 DOI: 10.1038/s41598-018-30768-0] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Accepted: 07/30/2018] [Indexed: 11/09/2022] Open
Abstract
Although there is a huge progress in complementary-metal-oxide-semiconductor (CMOS) technology, construction of an artificial neural network using CMOS technology to realize the functionality comparable with that of human cerebral cortex containing 1010-1011 neurons is still of great challenge. Recently, phase change memristor neuron has been proposed to realize a human-brain level neural network operating at a high speed while consuming a small amount of power and having a high integration density. Although memristor neuron can be scaled down to nanometer, integration of 1010-1011 neurons still faces many problems in circuit complexity, chip area, power consumption, etc. In this work, we propose a CMOS compatible HfO2 memristor neuron that can be well integrated with silicon circuits. A hybrid Convolutional Neural Network (CNN) based on the HfO2 memristor neuron is proposed and constructed. In the hybrid CNN, one memristive neuron can behave as multiple physical neurons based on the Time Division Multiplexing Access (TDMA) technique. Handwritten digit recognition is demonstrated in the hybrid CNN with a memristive neuron acting as 784 physical neurons. This work paves the way towards substantially shrinking the amount of neurons required in hardware and realization of more complex or even human cerebral cortex level memristive neural networks.
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Affiliation(s)
- J J Wang
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, P. R. China
| | - S G Hu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, P. R. China
| | - X T Zhan
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, P. R. China
| | - Q Yu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, P. R. China
| | - Z Liu
- School of Materials and Energy, Guangdong University of Technology, Guangzhou, 510006, P. R. China
| | - T P Chen
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Y Yin
- Graduate School of Engineering, Gunma University, 1-5-1Tenjin, Kiryu, Gunma, 376-8515, Japan
| | - Sumio Hosaka
- Graduate School of Engineering, Gunma University, 1-5-1Tenjin, Kiryu, Gunma, 376-8515, Japan
| | - Y Liu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, P. R. China.
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48
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Bayat FM, Prezioso M, Chakrabarti B, Nili H, Kataeva I, Strukov D. Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits. Nat Commun 2018; 9:2331. [PMID: 29899421 PMCID: PMC5998062 DOI: 10.1038/s41467-018-04482-4] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Accepted: 05/02/2018] [Indexed: 11/09/2022] Open
Abstract
The progress in the field of neural computation hinges on the use of hardware more efficient than the conventional microprocessors. Recent works have shown that mixed-signal integrated memristive circuits, especially their passive (0T1R) variety, may increase the neuromorphic network performance dramatically, leaving far behind their digital counterparts. The major obstacle, however, is immature memristor technology so that only limited functionality has been reported. Here we demonstrate operation of one-hidden layer perceptron classifier entirely in the mixed-signal integrated hardware, comprised of two passive 20 × 20 metal-oxide memristive crossbar arrays, board-integrated with discrete conventional components. The demonstrated network, whose hardware complexity is almost 10× higher as compared to previously reported functional classifier circuits based on passive memristive crossbars, achieves classification fidelity within 3% of that obtained in simulations, when using ex-situ training. The successful demonstration was facilitated by improvements in fabrication technology of memristors, specifically by lowering variations in their I-V characteristics.
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Affiliation(s)
- F Merrikh Bayat
- Electrical and Computer Engineering Department, University of California, Santa Barbara, CA, 93117, USA
| | - M Prezioso
- Electrical and Computer Engineering Department, University of California, Santa Barbara, CA, 93117, USA
| | - B Chakrabarti
- Electrical and Computer Engineering Department, University of California, Santa Barbara, CA, 93117, USA
| | - H Nili
- Electrical and Computer Engineering Department, University of California, Santa Barbara, CA, 93117, USA
| | - I Kataeva
- DENSO CORP, 500-1 Minamiyama, Komenoki-cho, Nisshin, 470-0111, Japan.
| | - D Strukov
- Electrical and Computer Engineering Department, University of California, Santa Barbara, CA, 93117, USA.
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49
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Hansen M, Zahari F, Kohlstedt H, Ziegler M. Unsupervised Hebbian learning experimentally realized with analogue memristive crossbar arrays. Sci Rep 2018; 8:8914. [PMID: 29892090 PMCID: PMC5995917 DOI: 10.1038/s41598-018-27033-9] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 05/22/2018] [Indexed: 11/21/2022] Open
Abstract
Conventional transistor electronics are reaching their limits in terms of scalability, power dissipation, and the underlying Boolean system architecture. To overcome this obstacle neuromorphic analogue systems are recently highly investigated. Particularly, the use of memristive devices in VLSI analogue concepts provides a promising pathway to realize novel bio-inspired computing architectures, which are able to unravel the foreseen difficulties of traditional electronics. Currently, a variety of materials and device structures are being studied along with novel computing schemes to make use of the attractive features of memristive devices for neuromorphic computing. However, a number of obstacles still have to be overcome to cast memristive devices into hardware systems. Most important is a physical implementation of memristive devices, which can cope with the high complexity of neural networks. This includes the integration of analogue and electroforming-free memristive devices into crossbar structures with no additional electronic components, such as selector devices. Here, an unsupervised, bio-motivated Hebbian based learning platform for visual pattern recognition is presented. The heart of the system is a crossbar array (16 × 16) which consists of selector-free and forming-free (non-filamentary) memristive devices, which exhibit analogue I-V characteristics.
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Affiliation(s)
- Mirko Hansen
- Nanoelektronik, Technische Fakultät Kiel, Christian-Albrechts-Universität Kiel, Kiel, 24143, Germany
| | - Finn Zahari
- Nanoelektronik, Technische Fakultät Kiel, Christian-Albrechts-Universität Kiel, Kiel, 24143, Germany
| | - Hermann Kohlstedt
- Nanoelektronik, Technische Fakultät Kiel, Christian-Albrechts-Universität Kiel, Kiel, 24143, Germany
| | - Martin Ziegler
- Nanoelektronik, Technische Fakultät Kiel, Christian-Albrechts-Universität Kiel, Kiel, 24143, Germany.
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50
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He HK, Yang R, Zhou W, Huang HM, Xiong J, Gan L, Zhai TY, Guo X. Photonic Potentiation and Electric Habituation in Ultrathin Memristive Synapses Based on Monolayer MoS 2. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2018; 14:e1800079. [PMID: 29504245 DOI: 10.1002/smll.201800079] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Indexed: 05/22/2023]
Abstract
Monolayer of 2D transition metal dichalcogenides, with a thickness of less than 1 nm, paves a feasible path to the development of ultrathin memristive synapses, to fulfill the requirements for constructing large-scale high density 3D stacking neuromorphic chips. Herein, memristive devices based on monolayer n-MoS2 on p-Si substrate with a large self-rectification ratio, exhibiting photonic potentiation and electric habituation, are successfully fabricated. Versatile synaptic neuromorphic functions, such as potentiation/habituation, short-term/long-term plasticity, and paired-pulse facilitation, are successfully mimicked based on the inherent persistent photoconductivity performance and the volatile resistive switching behavior. These findings demonstrate the potential applications of ultrathin transition metal dichalcogenides for memristive synapses. These memristive synapses with the combination of photonic and electric neuromorphic functions have prospective in the applications of synthetic retinas and optoelectronic interfaces for integrated photonic circuits based on mixed-mode electro-optical operation.
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Affiliation(s)
- Hui-Kai He
- School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Rui Yang
- School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Wen Zhou
- School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - He-Ming Huang
- School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Jue Xiong
- School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Lin Gan
- School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Tian-You Zhai
- School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
| | - Xin Guo
- School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China
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