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Wang X, Li H. Reservoir computing with a random memristor crossbar array. NANOTECHNOLOGY 2024; 35:415205. [PMID: 38991518 DOI: 10.1088/1361-6528/ad61ee] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 07/11/2024] [Indexed: 07/13/2024]
Abstract
Physical implementations of reservoir computing (RC) based on the emerging memristors have become promising candidates of unconventional computing paradigms. Traditionally, sequential approaches by time-multiplexing volatile memristors have been prevalent because of their low hardware overhead. However, they suffer from the problem of speed degradation and fall short of capturing the spatial relationship between the time-domain inputs. Here, we explore a new avenue for RC using memristor crossbar arrays with device-to-device variations, which serve as physical random weight matrices of the reservoir layers, enabling faster computation thanks to the parallelism of matrix-vector multiplication as an intensive operation in RC. To achieve this new RC architecture, ultralow-current, self-selective memristors are fabricated and integrated without the need of transistors, showing greater potential of high scalability and three-dimensional integrability compared to the previous realizations. The information processing ability of our RC system is demonstrated in asks of recognizing digit images and waveforms. This work indicates that the 'nonidealities' of the emerging memristor devices and circuits are a useful source of inspiration for new computing paradigms.
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Affiliation(s)
- Xinxin Wang
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing 100084, People's Republic of China
| | - Huanglong Li
- Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing 100084, People's Republic of China
- Chinese Institute for Brain Research, Beijing 102206, People's Republic of China
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Zhang QR, Ouyang WL, Wang XM, Yang F, Chen JG, Wen ZX, Liu JX, Wang G, Liu Q, Liu FC. Dynamic memristor for physical reservoir computing. NANOSCALE 2024; 16:13847-13860. [PMID: 38984618 DOI: 10.1039/d4nr01445f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
Reservoir computing (RC) has attracted considerable attention for its efficient handling of temporal signals and lower training costs. As a nonlinear dynamic system, RC can map low-dimensional inputs into high-dimensional spaces and implement classification using a simple linear readout layer. The memristor exhibits complex dynamic characteristics due to its internal physical processes, which renders them an ideal choice for the implementation of physical reservoir computing (PRC) systems. This review focuses on PRC systems based on memristors, explaining the resistive switching mechanism at the device level and emphasizing the tunability of their dynamic behavior. The development of memristor-based reservoir computing systems is highlighted, along with discussions on the challenges faced by this field and potential future research directions.
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Affiliation(s)
- Qi-Rui Zhang
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313099, China.
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei-Lun Ouyang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xue-Mei Wang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fan Yang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jian-Gang Chen
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhi-Xing Wen
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313099, China.
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jia-Xin Liu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Ge Wang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Qing Liu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fu-Cai Liu
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313099, China.
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
- 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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Armendarez NX, Mohamed AS, Dhungel A, Hossain MR, Hasan MS, Najem JS. Brain-Inspired Reservoir Computing Using Memristors with Tunable Dynamics and Short-Term Plasticity. ACS APPLIED MATERIALS & INTERFACES 2024; 16:6176-6188. [PMID: 38271202 DOI: 10.1021/acsami.3c16003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
Recent advancements in reservoir computing (RC) research have created a demand for analogue devices with dynamics that can facilitate the physical implementation of reservoirs, promising faster information processing while consuming less energy and occupying a smaller area footprint. Studies have demonstrated that dynamic memristors, with nonlinear and short-term memory dynamics, are excellent candidates as information-processing devices or reservoirs for temporal classification and prediction tasks. Previous implementations relied on nominally identical memristors that applied the same nonlinear transformation to the input data, which is not enough to achieve a rich state space. To address this limitation, researchers either diversified the data encoding across multiple memristors or harnessed the stochastic device-to-device variability among the memristors. However, this approach requires additional preprocessing steps and leads to synchronization issues. Instead, it is preferable to encode the data once and pass them through a reservoir layer consisting of memristors with distinct dynamics. Here, we demonstrate that ion-channel-based memristors with voltage-dependent dynamics can be controllably and predictively tuned through the voltage or adjustment of the ion channel concentration to exhibit diverse dynamic properties. We show, through experiments and simulations, that reservoir layers constructed with a small number of distinct memristors exhibit significantly higher predictive and classification accuracies with a single data encoding. We found that for a second-order nonlinear dynamical system prediction task, the varied memristor reservoir experimentally achieved an impressive normalized mean square error of 1.5 × 10-3, using only five distinct memristors. Moreover, in a neural activity classification task, a reservoir of just three distinct memristors experimentally attained an accuracy of 96.5%. This work lays the foundation for next-generation physical RC systems that can exploit the complex dynamics of their diverse building blocks to achieve increased signal processing capabilities.
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Affiliation(s)
- Nicholas X Armendarez
- Department of Mechanical Engineering, The Pennsylvania State University, 336 Reber Building, University Park, Pennsylvania 16802, United States
| | - Ahmed S Mohamed
- Department of Mechanical Engineering, The Pennsylvania State University, 336 Reber Building, University Park, Pennsylvania 16802, United States
| | - Anurag Dhungel
- Department of Electrical and Computer Engineering, The University of Mississippi, 310 Anderson Hall, University, Mississippi 38677, United States
| | - Md Razuan Hossain
- Department of Electrical and Computer Engineering, The University of Mississippi, 310 Anderson Hall, University, Mississippi 38677, United States
| | - Md Sakib Hasan
- Department of Electrical and Computer Engineering, The University of Mississippi, 310 Anderson Hall, University, Mississippi 38677, United States
| | - Joseph S Najem
- Department of Mechanical Engineering, The Pennsylvania State University, 336 Reber Building, University Park, Pennsylvania 16802, United States
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