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Baek IK, Lee SH, Jang YH, Park H, Kim J, Cheong S, Shim SK, Han J, Han JK, Jeon GS, Shin DH, Woo KS, Hwang CS. Implementation of Bayesian networks and Bayesian inference using a Cu 0.1Te 0.9/HfO 2/Pt threshold switching memristor. NANOSCALE ADVANCES 2024; 6:2892-2902. [PMID: 38817425 PMCID: PMC11134254 DOI: 10.1039/d3na01166f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Accepted: 04/04/2024] [Indexed: 06/01/2024]
Abstract
Bayesian networks and Bayesian inference, which forecast uncertain causal relationships within a stochastic framework, are used in various artificial intelligence applications. However, implementing hardware circuits for the Bayesian inference has shortcomings regarding device performance and circuit complexity. This work proposed a Bayesian network and inference circuit using a Cu0.1Te0.9/HfO2/Pt volatile memristor, a probabilistic bit neuron that can control the probability of being 'true' or 'false.' Nodal probabilities within the network are feasibly sampled with low errors, even with the device's cycle-to-cycle variations. Furthermore, Bayesian inference of all conditional probabilities within the network is implemented with low power (<186 nW) and energy consumption (441.4 fJ), and a normalized mean squared error of ∼7.5 × 10-4 through division feedback logic with a variational learning rate to suppress the inherent variation of the memristor. The suggested memristor-based Bayesian network shows the potential to replace the conventional complementary metal oxide semiconductor-based Bayesian estimation method with power efficiency using a stochastic computing method.
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Affiliation(s)
- In Kyung Baek
- Department of Materials Science and Engineering, and Inter-University Semiconductor Research Center, Seoul National University Seoul 08826 Republic of Korea
| | - Soo Hyung Lee
- Department of Materials Science and Engineering, and Inter-University Semiconductor Research Center, Seoul National University Seoul 08826 Republic of Korea
| | - Yoon Ho Jang
- Department of Materials Science and Engineering, and Inter-University Semiconductor Research Center, Seoul National University Seoul 08826 Republic of Korea
| | - Hyungjun Park
- Department of Materials Science and Engineering, and Inter-University Semiconductor Research Center, Seoul National University Seoul 08826 Republic of Korea
| | - Jaehyun Kim
- Department of Materials Science and Engineering, and Inter-University Semiconductor Research Center, Seoul National University Seoul 08826 Republic of Korea
| | - Sunwoo Cheong
- Department of Materials Science and Engineering, and Inter-University Semiconductor Research Center, Seoul National University Seoul 08826 Republic of Korea
| | - Sung Keun Shim
- Department of Materials Science and Engineering, and Inter-University Semiconductor Research Center, Seoul National University Seoul 08826 Republic of Korea
| | - Janguk Han
- Department of Materials Science and Engineering, and Inter-University Semiconductor Research Center, Seoul National University Seoul 08826 Republic of Korea
| | - Joon-Kyu Han
- System Semiconductor Engineering and Department of Electronic Engineering, Sogang University 35 Baekbeom-ro, Mapo-gu Seoul 04107 Republic of Korea
| | - Gwang Sik Jeon
- Department of Materials Science and Engineering, and Inter-University Semiconductor Research Center, Seoul National University Seoul 08826 Republic of Korea
| | - Dong Hoon Shin
- Department of Materials Science and Engineering, and Inter-University Semiconductor Research Center, Seoul National University Seoul 08826 Republic of Korea
| | - Kyung Seok Woo
- Department of Materials Science and Engineering, and Inter-University Semiconductor Research Center, Seoul National University Seoul 08826 Republic of Korea
| | - Cheol Seong Hwang
- Department of Materials Science and Engineering, and Inter-University Semiconductor Research Center, Seoul National University Seoul 08826 Republic of Korea
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Meng J, Song J, Fang Y, Wang T, Zhu H, Ji L, Sun QQ, Zhang DW, Chen L. Ionic Diffusive Nanomemristors with Dendritic Competition and Cooperation Functions for Ultralow Voltage Neuromorphic Computing. ACS NANO 2024; 18:9150-9159. [PMID: 38477708 DOI: 10.1021/acsnano.4c00424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
Realization of dendric signal processing in the human brain is of great significance for spatiotemporal neuromorphic engineering. Here, we proposed an ionic dendrite device with multichannel communication, which could realize synaptic behaviors even under an ultralow action potential of 80 mV. The device not only could simulate one-to-one information transfer of axons but also achieve a many-to-one modulation mode of dendrites. By the adjustment of two presynapses, Pavlov's dog conditioning experiment was learned successfully. Furthermore, the device also could emulate the biological synaptic competition and synaptic cooperation phenomenon through the comodulation of three presynapses, which are crucial for artificial neural network (ANN) implementation. Finally, an ANN was further constructed to realize highly efficient and anti-interference recognition of fashion patterns. By introducing the cooperative device, synaptic weight updates could be improved for higher linearity and larger dynamic regulation range in neuromorphic computing, resulting in higher recognition accuracy and efficiency. Such an artificial dendric device has great application prospects in the processing of more complex information and the construction of an ANN system with more functions.
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Affiliation(s)
- Jialin Meng
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - Jieru Song
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Yuqing Fang
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
| | - Tianyu Wang
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - Hao Zhu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - Li Ji
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - Qing-Qing Sun
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - David Wei Zhang
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
| | - Lin Chen
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
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Choi S, Moon T, Wang G, Yang JJ. Filament-free memristors for computing. NANO CONVERGENCE 2023; 10:58. [PMID: 38110639 PMCID: PMC10728429 DOI: 10.1186/s40580-023-00407-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 12/06/2023] [Indexed: 12/20/2023]
Abstract
Memristors have attracted increasing attention due to their tremendous potential to accelerate data-centric computing systems. The dynamic reconfiguration of memristive devices in response to external electrical stimuli can provide highly desirable novel functionalities for computing applications when compared with conventional complementary-metal-oxide-semiconductor (CMOS)-based devices. Those most intensively studied and extensively reviewed memristors in the literature so far have been filamentary type memristors, which typically exhibit a relatively large variability from device to device and from switching cycle to cycle. On the other hand, filament-free switching memristors have shown a better uniformity and attractive dynamical properties, which can enable a variety of new computing paradigms but have rarely been reviewed. In this article, a wide range of filament-free switching memristors and their corresponding computing applications are reviewed. Various junction structures, switching properties, and switching principles of filament-free memristors are surveyed and discussed. Furthermore, we introduce recent advances in different computing schemes and their demonstrations based on non-filamentary memristors. This Review aims to present valuable insights and guidelines regarding the key computational primitives and implementations enabled by these filament-free switching memristors.
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Affiliation(s)
- Sanghyeon Choi
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, 93106, USA
| | - Taehwan Moon
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Gunuk Wang
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Department of Integrative Energy Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
- Center for Neuromorphic Engineering, Korea Institute of Science and Technology, Seoul, 02792, Republic of Korea
| | - J Joshua Yang
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA.
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4
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Rhee H, Kim G, Song H, Park W, Kim DH, In JH, Lee Y, Kim KM. Probabilistic computing with NbO x metal-insulator transition-based self-oscillatory pbit. Nat Commun 2023; 14:7199. [PMID: 37938550 PMCID: PMC10632392 DOI: 10.1038/s41467-023-43085-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 10/30/2023] [Indexed: 11/09/2023] Open
Abstract
Energy-based computing is a promising approach for addressing the rising demand for solving NP-hard problems across diverse domains, including logistics, artificial intelligence, cryptography, and optimization. Probabilistic computing utilizing pbits, which can be manufactured using the semiconductor process and seamlessly integrated with conventional processing units, stands out as an efficient candidate to meet these demands. Here, we propose a novel pbit unit using an NbOx volatile memristor-based oscillator capable of generating probabilistic bits in a self-clocking manner. The noise-induced metal-insulator transition causes the probabilistic behavior, which can be effectively modeled using a multi-noise-induced stochastic process around the metal-insulator transition temperature. We demonstrate a memristive Boltzmann machine based on our proposed pbit and validate its feasibility by solving NP-hard problems. Furthermore, we propose a streamlined operation methodology that considers the autocorrelation of individual bits, enabling energy-efficient and high-performance probabilistic computing.
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Affiliation(s)
- Hakseung Rhee
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Gwangmin Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Hanchan Song
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Woojoon Park
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Do Hoon Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Jae Hyun In
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Younghyun Lee
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Kyung Min Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
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5
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Li J, Abbas H, Ang DS, Ali A, Ju X. Emerging memristive artificial neuron and synapse devices for the neuromorphic electronics era. NANOSCALE HORIZONS 2023; 8:1456-1484. [PMID: 37615055 DOI: 10.1039/d3nh00180f] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Abstract
Growth of data eases the way to access the world but requires increasing amounts of energy to store and process. Neuromorphic electronics has emerged in the last decade, inspired by biological neurons and synapses, with in-memory computing ability, extenuating the 'von Neumann bottleneck' between the memory and processor and offering a promising solution to reduce the efforts both in data storage and processing, thanks to their multi-bit non-volatility, biology-emulated characteristics, and silicon compatibility. This work reviews the recent advances in emerging memristive devices for artificial neuron and synapse applications, including memory and data-processing ability: the physics and characteristics are discussed first, i.e., valence changing, electrochemical metallization, phase changing, interfaced-controlling, charge-trapping, ferroelectric tunnelling, and spin-transfer torquing. Next, we propose a universal benchmark for the artificial synapse and neuron devices on spiking energy consumption, standby power consumption, and spike timing. Based on the benchmark, we address the challenges, suggest the guidelines for intra-device and inter-device design, and provide an outlook for the neuromorphic applications of resistive switching-based artificial neuron and synapse devices.
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Affiliation(s)
- Jiayi Li
- School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.
| | - Haider Abbas
- School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.
| | - Diing Shenp Ang
- School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.
| | - Asif Ali
- School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.
| | - Xin Ju
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore 138634
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6
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Liu H, Qin Y, Chen HY, Wu J, Ma J, Du Z, Wang N, Zou J, Lin S, Zhang X, Zhang Y, Wang H. Artificial Neuronal Devices Based on Emerging Materials: Neuronal Dynamics and Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2205047. [PMID: 36609920 DOI: 10.1002/adma.202205047] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 12/02/2022] [Indexed: 06/17/2023]
Abstract
Artificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have proposed a range of device concepts that can mimic neuronal dynamics and functions. Although the switching physics and device structures of these artificial neurons are largely different, their behaviors can be described by several neuron models in a more unified manner. In this paper, the reports of artificial neuronal devices based on emerging volatile switching materials are reviewed from the perspective of the demonstrated neuron models, with a focus on the neuronal functions implemented in these devices and the exploitation of these functions for computational and sensing applications. Furthermore, the neuroscience inspirations and engineering methods to enrich the neuronal dynamics that remain to be implemented in artificial neuronal devices and networks toward realizing the full functionalities of biological neurons are discussed.
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Affiliation(s)
- Hefei Liu
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Yuan Qin
- Center for Power Electronics Systems, Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24060, USA
| | - Hung-Yu Chen
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jiangbin Wu
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jiahui Ma
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Zhonghao Du
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Nan Wang
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
| | - Jingyi Zou
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Sen Lin
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Xu Zhang
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Yuhao Zhang
- Center for Power Electronics Systems, Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24060, USA
| | - Han Wang
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
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7
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Wang T, Meng J, Zhou X, Liu Y, He Z, Han Q, Li Q, Yu J, Li Z, Liu Y, Zhu H, Sun Q, Zhang DW, Chen P, Peng H, Chen L. Reconfigurable neuromorphic memristor network for ultralow-power smart textile electronics. Nat Commun 2022; 13:7432. [PMID: 36460675 PMCID: PMC9718838 DOI: 10.1038/s41467-022-35160-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 11/21/2022] [Indexed: 12/04/2022] Open
Abstract
Neuromorphic computing memristors are attractive to construct low-power- consumption electronic textiles due to the intrinsic interwoven architecture and promising applications in wearable electronics. Developing reconfigurable fiber-based memristors is an efficient method to realize electronic textiles that capable of neuromorphic computing function. However, the previously reported artificial synapse and neuron need different materials and configurations, making it difficult to realize multiple functions in a single device. Herein, a textile memristor network of Ag/MoS2/HfAlOx/carbon nanotube with reconfigurable characteristics was reported, which can achieve both nonvolatile synaptic plasticity and volatile neuron functions. In addition, a single reconfigurable memristor can realize integrate-and-fire function, exhibiting significant advantages in reducing the complexity of neuron circuits. The firing energy consumption of fiber-based memristive neuron is 1.9 fJ/spike (femtojoule-level), which is at least three orders of magnitude lower than that of the reported biological and artificial neuron (picojoule-level). The ultralow energy consumption makes it possible to create an electronic neural network that reduces the energy consumption compared to human brain. By integrating the reconfigurable synapse, neuron and heating resistor, a smart textile system is successfully constructed for warm fabric application, providing a unique functional reconfiguration pathway toward the next-generation in-memory computing textile system.
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Affiliation(s)
- Tianyu Wang
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Jialin Meng
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Xufeng Zhou
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, 200438 Shanghai, China
| | - Yue Liu
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, 200438 Shanghai, China
| | - Zhenyu He
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Qi Han
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Qingxuan Li
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Jiajie Yu
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Zhenhai Li
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Yongkai Liu
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Hao Zhu
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Qingqing Sun
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - David Wei Zhang
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
| | - Peining Chen
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, 200438 Shanghai, China
| | - Huisheng Peng
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, and Laboratory of Advanced Materials, Fudan University, 200438 Shanghai, China
| | - Lin Chen
- grid.8547.e0000 0001 0125 2443School of Microelectronics, Fudan University, 200433 Shanghai, China ,Zhangjiang Fudan International Innovation Center, 201203 Shanghai, China
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8
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Woo KS, Kim J, Han J, Kim W, Jang YH, Hwang CS. Probabilistic computing using Cu 0.1Te 0.9/HfO 2/Pt diffusive memristors. Nat Commun 2022; 13:5762. [PMID: 36180426 PMCID: PMC9525628 DOI: 10.1038/s41467-022-33455-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 09/19/2022] [Indexed: 11/17/2022] Open
Abstract
A computing scheme that can solve complex tasks is necessary as the big data field proliferates. Probabilistic computing (p-computing) paves the way to efficiently handle problems based on stochastic units called probabilistic bits (p-bits). This study proposes p-computing based on the threshold switching (TS) behavior of a Cu0.1Te0.9/HfO2/Pt (CTHP) diffusive memristor. The theoretical background of the p-computing resembling the Hopfield network structure is introduced to explain the p-computing system. P-bits are realized by the stochastic TS behavior of CTHP diffusive memristors, and they are connected to form the p-computing network. The memristor-based p-bit is likely to be ‘0’ and ‘1’, of which probability is controlled by an input voltage. The memristor-based p-computing enables all 16 Boolean logic operations in both forward and inverted operations, showing the possibility of expanding its uses for complex operations, such as full adder and factorization. Designing a computing scheme to solve complex tasks as the big data field proliferates remains a challenge. Here, the authors present a probabilistic bit generation hardware built using the random nature of CuxTe1−x/HfO2/Pt memristors capable of performing logic gates with invertible mode, showing the expandability to complex logic circuits.
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Affiliation(s)
- Kyung Seok Woo
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehag-dong, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Jaehyun Kim
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehag-dong, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Janguk Han
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehag-dong, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Woohyun Kim
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehag-dong, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Yoon Ho Jang
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehag-dong, Gwanak-gu, Seoul, 08826, Republic of Korea
| | - Cheol Seong Hwang
- Department of Materials Science and Engineering and Inter-University Semiconductor Research Center, Seoul National University, Gwanak-ro 1, Daehag-dong, Gwanak-gu, Seoul, 08826, Republic of Korea.
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9
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Choi S, Jang J, Kim MS, Kim ND, Kwag J, Wang G. Flexible Neural Network Realized by the Probabilistic SiO x Memristive Synaptic Array for Energy-Efficient Image Learning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2104773. [PMID: 35170246 PMCID: PMC9009121 DOI: 10.1002/advs.202104773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 12/31/2021] [Indexed: 06/14/2023]
Abstract
The human brain's neural networks are sparsely connected via tunable and probabilistic synapses, which may be essential for performing energy-efficient cognitive and intellectual functions. In this sense, the implementation of a flexible neural network with probabilistic synapses is a first step toward realizing the ultimate energy-efficient computing framework. Here, inspired by the efficient threshold-tunable and probabilistic rod-to-rod bipolar synapses in the human visual system, a 16 × 16 crossbar array comprising the vertical form of gate-tunable probabilistic SiOx memristive synaptic barristor utilizing the Si/graphene heterojunction is designed and fabricated. Controllable stochastic switching dynamics in this array are achieved via various input voltage pulse schemes. In particular, the threshold tunability via electrostatic gating enables the efficient in situ alteration of the probabilistic switching activation (PAct ) from 0 to 1.0, and can even modulate the degree of the PAct change. A drop-connected algorithm based on the PAct is constructed and used to successfully classify the shapes of several fashion items. The suggested approach can decrease the learning energy by up to ≈2,116 times relative to that of the conventional all-to-all connected network while exhibiting a high recognition accuracy of ≈93 %.
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Affiliation(s)
- Sanghyeon Choi
- KU‐KIST Graduate School of Converging Science and TechnologyKorea University145 Anam‐ro, Seongbuk‐guSeoul02841Republic of Korea
| | - Jingon Jang
- KU‐KIST Graduate School of Converging Science and TechnologyKorea University145 Anam‐ro, Seongbuk‐guSeoul02841Republic of Korea
| | - Min Seob Kim
- Institute of Advanced Composite MaterialsKorea Institute of Science and Technology92 Chudong‐ro, Bongdong‐eupWanju‐gunJeollabuk‐do55324Republic of Korea
| | - Nam Dong Kim
- Institute of Advanced Composite MaterialsKorea Institute of Science and Technology92 Chudong‐ro, Bongdong‐eupWanju‐gunJeollabuk‐do55324Republic of Korea
| | - Jeehyun Kwag
- Department of Brain and Cognitive EngineeringKorea UniversitySeoul02841Republic of Korea
| | - Gunuk Wang
- KU‐KIST Graduate School of Converging Science and TechnologyKorea University145 Anam‐ro, Seongbuk‐guSeoul02841Republic of Korea
- Department of Integrative Energy EngineeringKorea University145 Anam‐ro, Seongbuk‐guSeoul02841Republic of Korea
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