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Shreya S, Jenkins AS, Rezaeiyan Y, Li R, Böhnert T, Benetti L, Ferreira R, Moradi F, Farkhani H. Granular vortex spin-torque nano oscillator for reservoir computing. Sci Rep 2023; 13:16722. [PMID: 37794052 PMCID: PMC10550924 DOI: 10.1038/s41598-023-43923-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 09/30/2023] [Indexed: 10/06/2023] Open
Abstract
In this paper, we investigate the granularity in the free layer of the magnetic tunnel junctions (MTJ) and its potential to function as a reservoir for reservoir computing where grains act as oscillatory neurons while the device is in the vortex state. The input of the reservoir is applied in the form of a magnetic field which can pin the vortex core into different grains of the device in the magnetic vortex state. The oscillation frequency and MTJ resistance vary across different grains in a non-linear fashion making them great candidates to be served as the reservoir's outputs for classification objectives. Hence, we propose an experimentally validated area-efficient single granular vortex spin-torque nano oscillator (GV-STNO) device in which pinning sites work as random reservoirs that can emulate neuronal functions. We harness the nonlinear oscillation frequency and resistance exhibited by the vortex core granular pinning of the GV-STNO reservoir computing system to demonstrate waveform classification.
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Affiliation(s)
- S Shreya
- Electrical and Computer Engineering Department, Aarhus University, 8200, Aarhus, Denmark.
| | - A S Jenkins
- International Iberian Nanotechnology Laboratory (INL), Braga, Portugal
| | - Y Rezaeiyan
- Electrical and Computer Engineering Department, Aarhus University, 8200, Aarhus, Denmark
| | - R Li
- Electrical and Computer Engineering Department, Aarhus University, 8200, Aarhus, Denmark
| | - T Böhnert
- International Iberian Nanotechnology Laboratory (INL), Braga, Portugal
| | - L Benetti
- International Iberian Nanotechnology Laboratory (INL), Braga, Portugal
| | - R Ferreira
- International Iberian Nanotechnology Laboratory (INL), Braga, Portugal
| | - F Moradi
- Electrical and Computer Engineering Department, Aarhus University, 8200, Aarhus, Denmark
| | - H Farkhani
- Electrical and Computer Engineering Department, Aarhus University, 8200, Aarhus, Denmark.
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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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Wang C, Lee C, Roy K. Noise resilient leaky integrate-and-fire neurons based on multi-domain spintronic devices. Sci Rep 2022; 12:8361. [PMID: 35589802 PMCID: PMC9120456 DOI: 10.1038/s41598-022-12555-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 05/12/2022] [Indexed: 12/04/2022] Open
Abstract
The capability of emulating neural functionalities efficiently in hardware is crucial for building neuromorphic computing systems. While various types of neuro-mimetic devices have been investigated, it remains challenging to provide a compact device that can emulate spiking neurons. In this work, we propose a non-volatile spin-based device for efficiently emulating a leaky integrate-and-fire neuron. By incorporating an exchange-coupled composite free layer in spin-orbit torque magnetic tunnel junctions, multi-domain magnetization switching dynamics is exploited to realize gradual accumulation of membrane potential for a leaky integrate-and-fire neuron with compact footprints. The proposed device offers significantly improved scalability compared with previously proposed spin-based neuro-mimetic implementations while exhibiting high energy efficiency and good controllability. Moreover, the proposed neuron device exhibits a varying leak constant and a varying membrane resistance that are both dependent on the magnitude of the membrane potential. Interestingly, we demonstrate that such device-inspired dynamic behaviors can be incorporated to construct more robust spiking neural network models, and find improved resiliency against various types of noise injection scenarios. The proposed spintronic neuro-mimetic devices may potentially open up exciting opportunities for the development of efficient and robust neuro-inspired computational hardware.
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Affiliation(s)
- Cheng Wang
- Department of Electrical and Computer Engineering, Purdue University, West Lafayette, 47907, IN, USA.
| | - Chankyu Lee
- Department of Electrical and Computer Engineering, Purdue University, West Lafayette, 47907, IN, USA
| | - Kaushik Roy
- Department of Electrical and Computer Engineering, Purdue University, West Lafayette, 47907, IN, USA
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Li ZX, Geng XY, Wang J, Zhuge F. Emerging Artificial Neuron Devices for Probabilistic Computing. Front Neurosci 2021; 15:717947. [PMID: 34421528 PMCID: PMC8377243 DOI: 10.3389/fnins.2021.717947] [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: 05/31/2021] [Accepted: 07/19/2021] [Indexed: 11/13/2022] Open
Abstract
In recent decades, artificial intelligence has been successively employed in the fields of finance, commerce, and other industries. However, imitating high-level brain functions, such as imagination and inference, pose several challenges as they are relevant to a particular type of noise in a biological neuron network. Probabilistic computing algorithms based on restricted Boltzmann machine and Bayesian inference that use silicon electronics have progressed significantly in terms of mimicking probabilistic inference. However, the quasi-random noise generated from additional circuits or algorithms presents a major challenge for silicon electronics to realize the true stochasticity of biological neuron systems. Artificial neurons based on emerging devices, such as memristors and ferroelectric field-effect transistors with inherent stochasticity can produce uncertain non-linear output spikes, which may be the key to make machine learning closer to the human brain. In this article, we present a comprehensive review of the recent advances in the emerging stochastic artificial neurons (SANs) in terms of probabilistic computing. We briefly introduce the biological neurons, neuron models, and silicon neurons before presenting the detailed working mechanisms of various SANs. Finally, the merits and demerits of silicon-based and emerging neurons are discussed, and the outlook for SANs is presented.
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Affiliation(s)
- Zong-xiao Li
- 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
| | - Xiao-ying Geng
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- School of Materials Science and Engineering, Southwest University of Science and Technology, Mianyang, China
| | - Jingrui Wang
- Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- 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
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