1
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Tsuzuki S. Extreme value statistics of nerve transmission delay. PLoS One 2024; 19:e0306605. [PMID: 38968286 PMCID: PMC11226101 DOI: 10.1371/journal.pone.0306605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 06/20/2024] [Indexed: 07/07/2024] Open
Abstract
Delays in nerve transmission are an important topic in the field of neuroscience. Spike signals fired or received by the dendrites of a neuron travel from the axon to a presynaptic cell. The spike signal then triggers a chemical reaction at the synapse, wherein a presynaptic cell transfers neurotransmitters to the postsynaptic cell, regenerates electrical signals via a chemical reaction through ion channels, and transmits them to neighboring neurons. In the context of describing the complex physiological reaction process as a stochastic process, this study aimed to show that the distribution of the maximum time interval of spike signals follows extreme-order statistics. By considering the statistical variance in the time constant of the leaky Integrate-and-Fire model, a deterministic time evolution model for spike signals, we enabled randomness in the time interval of the spike signals. When the time constant follows an exponential distribution function, the time interval of the spike signal also follows an exponential distribution. In this case, our theory and simulations confirmed that the histogram of the maximum time interval follows the Gumbel distribution, one of the three forms of extreme-value statistics. We further confirmed that the histogram of the maximum time interval followed a Fréchet distribution when the time interval of the spike signal followed a Pareto distribution. These findings confirm that nerve transmission delay can be described using extreme value statistics and can therefore be used as a new indicator of transmission delay.
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Affiliation(s)
- Satori Tsuzuki
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
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2
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Kim DH, Cheong WH, Song H, Jeon JB, Kim G, Kim KM. Memristive Monte Carlo DropConnect crossbar array enabled by device and algorithm co-design. MATERIALS HORIZONS 2024. [PMID: 38916265 DOI: 10.1039/d3mh02049e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Device and algorithm co-design aims to develop energy-efficient hardware that directly implements complex algorithms and optimizes algorithms to match the hardware's characteristics. Specifically, neuromorphic computing algorithms are constantly growing in complexity, necessitating an ongoing search for hardware implementations capable of handling these intricate algorithms. Here, we present a memristive Monte Carlo DropConnect (MC-DC) crossbar array developed through a hardware algorithm co-design approach. To implement the MC-DC neural network, stochastic switching and analog memory characteristics are required, and we achieved them using Ag-based diffusive selectors and Ru-based electrochemical metalization (ECM) memristors, respectively. The devices were integrated with a one-selector one-memristor (1S1M) structure, and their well-matched operating voltages and currents enabled stochastic readout and deterministic analog programming. With the integrated hardware, we successfully demonstrated the MC-DC operation. Additionally, the selector allowed for the control of switching polarity, and by understanding this hardware characteristic, we were able to modify the algorithm to fit it and further improve the network performance.
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Affiliation(s)
- Do Hoon Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Woon Hyung Cheong
- Applied Science Research Institute, 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.
| | - Jae Bum Jeon
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Geunyoung Kim
- 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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3
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Hwang TG, Park H, Cho WJ. Organic-Inorganic Hybrid Synaptic Transistors: Methyl-Silsesquioxanes-Based Electric Double Layer for Enhanced Synaptic Functionality and CMOS Compatibility. Biomimetics (Basel) 2024; 9:157. [PMID: 38534842 DOI: 10.3390/biomimetics9030157] [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: 01/15/2024] [Revised: 02/29/2024] [Accepted: 02/29/2024] [Indexed: 03/28/2024] Open
Abstract
Electrical double-layer (EDL) synaptic transistors based on organic materials exhibit low thermal and chemical stability and are thus incompatible with complementary metal oxide semiconductor (CMOS) processes involving high-temperature operations. This paper proposes organic-inorganic hybrid synaptic transistors using methyl silsesquioxane (MSQ) as the electrolyte. MSQ, derived from the combination of inorganic silsesquioxanes and the organic methyl (-CH3) group, exhibits exceptional thermal and chemical stability, thus ensuring compatibility with CMOS processes. We fabricated Al/MSQ electrolyte/Pt capacitors, exhibiting a substantial capacitance of 1.89 µF/cm2 at 10 Hz. MSQ-based EDL synaptic transistors demonstrated various synaptic behaviors, such as excitatory post-synaptic current, paired-pulse facilitation, signal pass filtering, and spike-number-dependent plasticity. Additionally, we validated synaptic functions such as information storage and synapse weight adjustment, simulating brain synaptic operations through potentiation and depression. Notably, these synaptic operations demonstrated stability over five continuous operation cycles. Lastly, we trained a multi-layer artificial deep neural network (DNN) using a handwritten Modified National Institute of Standards and Technology image dataset. The DNN achieved an impressive recognition rate of 92.28%. The prepared MSQ-based EDL synaptic transistors, with excellent thermal/chemical stability, synaptic functionality, and compatibility with CMOS processes, harbor tremendous potential as materials for next-generation artificial synapse components.
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Affiliation(s)
- Tae-Gyu Hwang
- Department of Electronic Materials Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Republic of Korea
| | - Hamin Park
- Department of Electronic Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Republic of Korea
| | - Won-Ju Cho
- Department of Electronic Materials Engineering, Kwangwoon University, Gwangun-ro 20, Nowon-gu, Seoul 01897, Republic of Korea
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4
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Lu C, Meng J, Yu J, Song J, Wang T, Zhu H, Sun QQ, Zhang DW, Chen L. Novel Three-Dimensional Artificial Neural Network Based on an Eight-Layer Vertical Memristor with an Ultrahigh Rectify Ratio (>10 7) and an Ultrahigh Nonlinearity (>10 5) for Neuromorphic Computing. NANO LETTERS 2024; 24:2018-2024. [PMID: 38315050 DOI: 10.1021/acs.nanolett.3c04577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
In recent years, memristors have successfully demonstrated their significant potential in artificial neural networks (ANNs) and neuromorphic computing. Nonetheless, ANNs constructed by crossbar arrays suffer from cross-talk issues and low integration densities. Here, we propose an eight-layer three-dimensional (3D) vertical crossbar memristor with an ultrahigh rectify ratio (RR > 107) and an ultrahigh nonlinearity (>105) to overcome these limitations, which enables it to reach a >1 Tb array size without reading failure. Furthermore, the proposed 3D RRAM shows advanced endurance (>1010 cycles), retention (>104 s), and uniformity. In addition, several synaptic functions observed in the human brain were mimicked. On the basis of the advanced performance, we constructed a novel 3D ANN, whose learning efficiency and recognition accuracy were enhanced significantly compared with those of conventional single-layer ANNs. These findings hold promise for the development of highly efficient, precise, integrated, and stable VLSI neuromorphic computing systems.
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Affiliation(s)
- Chen Lu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Jialin Meng
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Jiajie Yu
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International 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
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
| | - Tianyu Wang
- School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, P. R. China
- Zhangjiang Fudan International 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
- Zhangjiang Fudan International 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
- Zhangjiang Fudan International 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
- Zhangjiang Fudan International 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
- Zhangjiang Fudan International Innovation Center, Shanghai 201203, China
- National Integrated Circuit Innovation Center, Shanghai 201203, China
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5
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Zhao J, Wang Y, Gao P, Li S, Peng Y. Synchronization of Complex Dynamical Networks with Stochastic Links Dynamics. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1457. [PMID: 37895577 PMCID: PMC10606096 DOI: 10.3390/e25101457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/06/2023] [Accepted: 10/15/2023] [Indexed: 10/29/2023]
Abstract
The mean square synchronization problem of the complex dynamical network (CDN) with the stochastic link dynamics is investigated. In contrast to previous literature, the CDN considered in this paper can be viewed as consisting of two subsystems coupled to each other. One subsystem consists of all nodes, referred to as the nodes subsystem, and the other consists of all links, referred to as the network topology subsystem, where the weighted values can quantitatively reflect changes in the network's topology. Based on the above understanding of CDN, two vector stochastic differential equations with Brownian motion are used to model the dynamic behaviors of nodes and links, respectively. The control strategy incorporates not only the controller in the nodes but also the coupling term in the links, through which the CDN is synchronized in the mean-square sense. Meanwhile, the dynamic stochastic signal is proposed in this paper, which is regarded as the auxiliary reference tracking target of links, such that the links can track the reference target asymptotically when synchronization occurs in nodes. This implies that the eventual topological structure of CDN is stochastic. Finally, a comparison simulation example confirms the superiority of the control strategy in this paper.
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Affiliation(s)
- Juanxia Zhao
- School of Automation, Guangdong University of Technology, Guangzhou 510006, China; (J.Z.); (Y.W.); (Y.P.)
| | - Yinhe Wang
- School of Automation, Guangdong University of Technology, Guangzhou 510006, China; (J.Z.); (Y.W.); (Y.P.)
| | - Peitao Gao
- School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou 510665, China;
| | - Shengping Li
- MOE Key Laboratory of Intelligent Manufacturing, Shantou University, Shantou 515063, China
| | - Yi Peng
- School of Automation, Guangdong University of Technology, Guangzhou 510006, China; (J.Z.); (Y.W.); (Y.P.)
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6
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Guo Y, Duan W, Liu X, Wang X, Wang L, Duan S, Ma C, Li H. Generative complex networks within a dynamic memristor with intrinsic variability. Nat Commun 2023; 14:6134. [PMID: 37783711 PMCID: PMC10545788 DOI: 10.1038/s41467-023-41921-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 09/21/2023] [Indexed: 10/04/2023] Open
Abstract
Artificial neural networks (ANNs) have gained considerable momentum in the past decade. Although at first the main task of the ANN paradigm was to tune the connection weights in fixed-architecture networks, there has recently been growing interest in evolving network architectures toward the goal of creating artificial general intelligence. Lagging behind this trend, current ANN hardware struggles for a balance between flexibility and efficiency but cannot achieve both. Here, we report on a novel approach for the on-demand generation of complex networks within a single memristor where multiple virtual nodes are created by time multiplexing and the non-trivial topological features, such as small-worldness, are generated by exploiting device dynamics with intrinsic cycle-to-cycle variability. When used for reservoir computing, memristive complex networks can achieve a noticeable increase in memory capacity a and respectable performance boost compared to conventional reservoirs trivially implemented as fully connected networks. This work expands the functionality of memristors for ANN computing.
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Affiliation(s)
- Yunpeng Guo
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, 100084, China
| | - Wenrui Duan
- School of Instrument Science and Opto Electronics Engineering, Laboratory of Intelligent Microsystems, Beijing Information Science & Technology University, Beijing, 100101, China.
| | - Xue Liu
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, 100084, China.
- School of Integrated Circuits, Tsinghua University, Beijing, 100084, China.
| | - Xinxin Wang
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, 100084, China
| | - Lidan Wang
- School of Artificial Intelligence, Southwest University, Chongqing, 400715, China
| | - Shukai Duan
- School of Artificial Intelligence, Southwest University, Chongqing, 400715, China
| | - Cheng Ma
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, 100084, China.
| | - Huanglong Li
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, 100084, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
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7
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Qiu E, Salev P, Torres F, Navarro H, Dynes RC, Schuller IK. Stochastic transition in synchronized spiking nanooscillators. Proc Natl Acad Sci U S A 2023; 120:e2303765120. [PMID: 37695901 PMCID: PMC10515151 DOI: 10.1073/pnas.2303765120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 07/29/2023] [Indexed: 09/13/2023] Open
Abstract
This work reports that synchronization of Mott material-based nanoscale coupled spiking oscillators can be drastically different from that in conventional harmonic oscillators. We investigated the synchronization of spiking nanooscillators mediated by thermal interactions due to the close physical proximity of the devices. Controlling the driving voltage enables in-phase 1:1 and 2:1 integer synchronization modes between neighboring oscillators. Transition between these two integer modes occurs through an unusual stochastic synchronization regime instead of the loss of spiking coherence. In the stochastic synchronization regime, random length spiking sequences belonging to the 1:1 and 2:1 integer modes are intermixed. The occurrence of this stochasticity is an important factor that must be taken into account in the design of large-scale spiking networks for hardware-level implementation of novel computational paradigms such as neuromorphic and stochastic computing.
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Affiliation(s)
- Erbin Qiu
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA92093
- Department of Physics, Center for Advanced Nanoscience, University of California San Diego, La Jolla, CA92093
| | - Pavel Salev
- Department of Physics and Astronomy, University of Denver, Denver, CO80208
| | - Felipe Torres
- Departamento de Física, Facultad de Ciencias, Universidad de Chile, Santiago7800024, Chile
| | - Henry Navarro
- Department of Physics, Center for Advanced Nanoscience, University of California San Diego, La Jolla, CA92093
| | - Robert C. Dynes
- Department of Physics, Center for Advanced Nanoscience, University of California San Diego, La Jolla, CA92093
| | - Ivan K. Schuller
- Department of Physics, Center for Advanced Nanoscience, University of California San Diego, La Jolla, CA92093
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8
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Misra S, Bland LC, Cardwell SG, Incorvia JAC, James CD, Kent AD, Schuman CD, Smith JD, Aimone JB. Probabilistic Neural Computing with Stochastic Devices. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2204569. [PMID: 36395387 DOI: 10.1002/adma.202204569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 08/03/2022] [Indexed: 06/16/2023]
Abstract
The brain has effectively proven a powerful inspiration for the development of computing architectures in which processing is tightly integrated with memory, communication is event-driven, and analog computation can be performed at scale. These neuromorphic systems increasingly show an ability to improve the efficiency and speed of scientific computing and artificial intelligence applications. Herein, it is proposed that the brain's ubiquitous stochasticity represents an additional source of inspiration for expanding the reach of neuromorphic computing to probabilistic applications. To date, many efforts exploring probabilistic computing have focused primarily on one scale of the microelectronics stack, such as implementing probabilistic algorithms on deterministic hardware or developing probabilistic devices and circuits with the expectation that they will be leveraged by eventual probabilistic architectures. A co-design vision is described by which large numbers of devices, such as magnetic tunnel junctions and tunnel diodes, can be operated in a stochastic regime and incorporated into a scalable neuromorphic architecture that can impact a number of probabilistic computing applications, such as Monte Carlo simulations and Bayesian neural networks. Finally, a framework is presented to categorize increasingly advanced hardware-based probabilistic computing technologies.
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Affiliation(s)
- Shashank Misra
- Microsystems Engineering, Science and Applications, Sandia National Laboratories, Albuquerque, NM, 87123, USA
| | - Leslie C Bland
- Department of Physics, Temple University, Philadelphia, PA, 19122-1801, USA
| | - Suma G Cardwell
- Neural Exploration and Research Laboratory, Sandia National Laboratories, Albuquerque, NM, 87123, USA
| | - Jean Anne C Incorvia
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Conrad D James
- Microsystems Engineering, Science and Applications, Sandia National Laboratories, Albuquerque, NM, 87123, USA
| | - Andrew D Kent
- Department of Physics, New York University, New York, NY, 10003, USA
| | - Catherine D Schuman
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, 37996, USA
| | - J Darby Smith
- Neural Exploration and Research Laboratory, Sandia National Laboratories, Albuquerque, NM, 87123, USA
| | - James B Aimone
- Neural Exploration and Research Laboratory, Sandia National Laboratories, Albuquerque, NM, 87123, USA
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Singhal R, Saraswat V, Deshmukh S, Subramoney S, Somappa L, Baghini MS, Ganguly U. Enhanced regularization for on-chip training using analog and temporary memory weights. Neural Netw 2023; 165:1050-1057. [PMID: 37478527 DOI: 10.1016/j.neunet.2023.07.001] [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: 12/15/2022] [Revised: 05/17/2023] [Accepted: 07/02/2023] [Indexed: 07/23/2023]
Abstract
In-memory computing techniques are used to accelerate artificial neural network (ANN) training and inference tasks. Memory technology and architectural innovations allow efficient matrix-vector multiplications, gradient calculations, and updates to network weights. However, on-chip learning for edge devices is quite challenging due to the frequent updates. Here, we propose using an analog and temporary on-chip memory (ATOM) cell with controllable retention timescales for implementing the weights of an on-chip training task. Measurement results for Read-Write timescales are presented for an ATOM cell fabricated in GlobalFoundries' 45 nm RFSOI technology. The effect of limited retention and its variability is evaluated for training a fully connected neural network with a variable number of layers for the MNIST hand-written digit recognition task. Our studies show that weight decay due to temporary memory can have benefits equivalent to regularization, achieving a ∼33% reduction in the validation error (from 3.6% to 2.4%). We also show that the controllability of the decay timescale can be advantageous in achieving a further ∼26% reduction in the validation error. This strongly suggests the utility of temporary memory during learning before on-chip non-volatile memories can take over for the storage and inference tasks using the neural network weights. We thus propose an algorithm-circuit codesign in the form of temporary analog memory for high-performing on-chip learning of ANNs.
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Affiliation(s)
- Raghav Singhal
- Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India.
| | - Vivek Saraswat
- Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Shreyas Deshmukh
- Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | | | - Laxmeesha Somappa
- Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Maryam Shojaei Baghini
- Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Udayan Ganguly
- Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India
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10
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Shan L, Chen Q, Yu R, Gao C, Liu L, Guo T, Chen H. A sensory memory processing system with multi-wavelength synaptic-polychromatic light emission for multi-modal information recognition. Nat Commun 2023; 14:2648. [PMID: 37156788 PMCID: PMC10167252 DOI: 10.1038/s41467-023-38396-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 04/25/2023] [Indexed: 05/10/2023] Open
Abstract
Realizing multi-modal information recognition tasks which can process external information efficiently and comprehensively is an urgent requirement in the field of artificial intelligence. However, it remains a challenge to achieve simple structure and high-performance multi-modal recognition demonstrations owing to the complex execution module and separation of memory processing based on the traditional complementary metal oxide semiconductor (CMOS) architecture. Here, we propose an efficient sensory memory processing system (SMPS), which can process sensory information and generate synapse-like and multi-wavelength light-emitting output, realizing diversified utilization of light in information processing and multi-modal information recognition. The SMPS exhibits strong robustness in information encoding/transmission and the capability of visible information display through the multi-level color responses, which can implement the multi-level pain warning process of organisms intuitively. Furthermore, different from the conventional multi-modal information processing system that requires independent and complex circuit modules, the proposed SMPS with unique optical multi-information parallel output can realize efficient multi-modal information recognition of dynamic step frequency and spatial positioning simultaneously with the accuracy of 99.5% and 98.2%, respectively. Therefore, the SMPS proposed in this work with simple component, flexible operation, strong robustness, and highly efficiency is promising for future sensory-neuromorphic photonic systems and interactive artificial intelligence.
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Affiliation(s)
- Liuting Shan
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
| | - Qizhen Chen
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen, 361024, China
| | - Rengjian Yu
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
| | - Changsong Gao
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
| | - Lujian Liu
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
| | - Tailiang Guo
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China
| | - Huipeng Chen
- Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China.
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China.
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11
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Seok H, Son S, Jathar SB, Lee J, Kim T. Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:3118. [PMID: 36991829 PMCID: PMC10058286 DOI: 10.3390/s23063118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/11/2023] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture. As computing operations based on von Neumann hardware rely on continuous memory transport between processing units and memory, fundamental limitations arise in terms of power consumption and integration density. In biological synapses, chemical stimulation induces information transfer from the pre- to the post-neuron. The memristor operates as resistive random-access memory (RRAM) and is incorporated into the hardware for neuromorphic computing. Hardware composed of synaptic memristor arrays is expected to lead to further breakthroughs owing to their biomimetic in-memory processing capabilities, low power consumption, and amenability to integration; these aspects satisfy the upcoming demands of artificial intelligence for higher computational loads. Among the tremendous efforts toward achieving human-brain-like electronics, layered 2D materials have demonstrated significant potential owing to their outstanding electronic and physical properties, facile integration with other materials, and low-power computing. This review discusses the memristive characteristics of various 2D materials (heterostructures, defect-engineered materials, and alloy materials) used in neuromorphic computing for image segregation or pattern recognition. Neuromorphic computing, the most powerful artificial networks for complicated image processing and recognition, represent a breakthrough in artificial intelligence owing to their enhanced performance and lower power consumption compared with von Neumann architectures. A hardware-implemented CNN with weight control based on synaptic memristor arrays is expected to be a promising candidate for future electronics in society, offering a solution based on non-von Neumann hardware. This emerging paradigm changes the computing algorithm using entirely hardware-connected edge computing and deep neural networks.
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Affiliation(s)
- Hyunho Seok
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Shihoon Son
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Sagar Bhaurao Jathar
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Jaewon Lee
- School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Taesung Kim
- SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Nano Science and Technology, Sungkyunkwan University, Suwon 16419, Republic of Korea
- School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
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12
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Chekol SA, Nacke R, Aussen S, Hoffmann-Eifert S. SET Kinetics of Ag/HfO 2-Based Diffusive Memristors under Various Counter-Electrode Materials. MICROMACHINES 2023; 14:571. [PMID: 36984978 PMCID: PMC10060002 DOI: 10.3390/mi14030571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 02/21/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
The counter-electrode (CE) material in electrochemical metallization memory (ECM) cells plays a crucial role in the switching process by affecting the reactions at the CE/electrolyte interface. This is due to the different electrocatalytic activity of the CE material towards reduction-oxidation reactions, which determines the metal ion concentration in the electrolyte and ultimately impacts the switching kinetics. In this study, the focus is laid on Pt, TiN, and W, which are relevant in standard chip technology. For these, the influence of CE metal on the switching kinetics of Ag/HfO2-based volatile ECM cells is investigated. Rectangular voltage pulses of different amplitudes were applied, and the SET times were analyzed from the transient curves. The results show that CE material has a significant effect on the SET kinetics, with differences being observed depending on the voltage regime. The formation of interfacial oxides at the CE/electrolyte interface, particularly for non-noble metals, is also discussed in relation to the findings. Overall, this work highlights the important role of the CE material in the switching process of Ag/HfO2-based diffusive memristors and the importance of considering interfacial oxide formation in the design of these devices.
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Affiliation(s)
- Solomon Amsalu Chekol
- Peter Grünberg Institute (PGI 7 and 10) and JARA-FIT, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
- Faculty of Georesources and Materials Engineering, RWTH Aachen University, Intzestraße 1, 52072 Aachen, Germany
| | - Richard Nacke
- Peter Grünberg Institute (PGI 7 and 10) and JARA-FIT, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
- Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, Templergraben 59, 52062 Aachen, Germany
| | - Stephan Aussen
- Peter Grünberg Institute (PGI 7 and 10) and JARA-FIT, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
- Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University, Templergraben 59, 52062 Aachen, Germany
| | - Susanne Hoffmann-Eifert
- Peter Grünberg Institute (PGI 7 and 10) and JARA-FIT, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
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