Realization of Artificial Neurons and Synapses Based on STDP Designed by an MTJ Device
As the third-generation neural network, the spiking neural network (SNN) has become one of the most promising neuromorphic computing paradigms to mimic brain neural networks over the past decade. The SNN shows many advantages in performing classification and recognition tasks in the artificial intel...
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MDPI AG
2023-09-01
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| Series: | Micromachines |
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| Online Access: | https://www.mdpi.com/2072-666X/14/10/1820 |
| _version_ | 1797572975249063936 |
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| author | Manman Wang Yuhai Yuan Yanfeng Jiang |
| author_facet | Manman Wang Yuhai Yuan Yanfeng Jiang |
| author_sort | Manman Wang |
| collection | DOAJ |
| description | As the third-generation neural network, the spiking neural network (SNN) has become one of the most promising neuromorphic computing paradigms to mimic brain neural networks over the past decade. The SNN shows many advantages in performing classification and recognition tasks in the artificial intelligence field. In the SNN, the communication between the pre-synapse neuron (PRE) and the post-synapse neuron (POST) is conducted by the synapse. The corresponding synaptic weights are dependent on both the spiking patterns of the PRE and the POST, which are updated by spike-timing-dependent plasticity (STDP) rules. The emergence and growing maturity of spintronic devices present a new approach for constructing the SNN. In the paper, a novel SNN is proposed, in which both the synapse and the neuron are mimicked with the spin transfer torque magnetic tunnel junction (STT-MTJ) device. The synaptic weight is presented by the conductance of the MTJ device. The mapping of the probabilistic spiking nature of the neuron to the stochastic switching behavior of the MTJ with thermal noise is presented based on the stochastic Landau–Lifshitz–Gilbert (LLG) equation. In this way, a simplified SNN is mimicked with the MTJ device. The function of the mimicked SNN is verified by a handwritten digit recognition task based on the MINIST database. |
| first_indexed | 2024-03-10T21:03:10Z |
| format | Article |
| id | doaj.art-fee1ad2756d44b8eacf40e892a3f720c |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-10T21:03:10Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-fee1ad2756d44b8eacf40e892a3f720c2023-11-19T17:23:17ZengMDPI AGMicromachines2072-666X2023-09-011410182010.3390/mi14101820Realization of Artificial Neurons and Synapses Based on STDP Designed by an MTJ DeviceManman Wang0Yuhai Yuan1Yanfeng Jiang2Department of Electrical Engineering, School of Internet of Things (IoTs), Jiangnan University, Wuxi 214122, ChinaDepartment of Electrical Engineering, School of Internet of Things (IoTs), Jiangnan University, Wuxi 214122, ChinaDepartment of Electrical Engineering, School of Internet of Things (IoTs), Jiangnan University, Wuxi 214122, ChinaAs the third-generation neural network, the spiking neural network (SNN) has become one of the most promising neuromorphic computing paradigms to mimic brain neural networks over the past decade. The SNN shows many advantages in performing classification and recognition tasks in the artificial intelligence field. In the SNN, the communication between the pre-synapse neuron (PRE) and the post-synapse neuron (POST) is conducted by the synapse. The corresponding synaptic weights are dependent on both the spiking patterns of the PRE and the POST, which are updated by spike-timing-dependent plasticity (STDP) rules. The emergence and growing maturity of spintronic devices present a new approach for constructing the SNN. In the paper, a novel SNN is proposed, in which both the synapse and the neuron are mimicked with the spin transfer torque magnetic tunnel junction (STT-MTJ) device. The synaptic weight is presented by the conductance of the MTJ device. The mapping of the probabilistic spiking nature of the neuron to the stochastic switching behavior of the MTJ with thermal noise is presented based on the stochastic Landau–Lifshitz–Gilbert (LLG) equation. In this way, a simplified SNN is mimicked with the MTJ device. The function of the mimicked SNN is verified by a handwritten digit recognition task based on the MINIST database.https://www.mdpi.com/2072-666X/14/10/1820STT-MTJneuronsynapseimage recognition |
| spellingShingle | Manman Wang Yuhai Yuan Yanfeng Jiang Realization of Artificial Neurons and Synapses Based on STDP Designed by an MTJ Device Micromachines STT-MTJ neuron synapse image recognition |
| title | Realization of Artificial Neurons and Synapses Based on STDP Designed by an MTJ Device |
| title_full | Realization of Artificial Neurons and Synapses Based on STDP Designed by an MTJ Device |
| title_fullStr | Realization of Artificial Neurons and Synapses Based on STDP Designed by an MTJ Device |
| title_full_unstemmed | Realization of Artificial Neurons and Synapses Based on STDP Designed by an MTJ Device |
| title_short | Realization of Artificial Neurons and Synapses Based on STDP Designed by an MTJ Device |
| title_sort | realization of artificial neurons and synapses based on stdp designed by an mtj device |
| topic | STT-MTJ neuron synapse image recognition |
| url | https://www.mdpi.com/2072-666X/14/10/1820 |
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