Bayesian Reasoning Enabled by Spin-Orbit Torque Magnetic Tunnel Junctions
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916685624115200 |
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| author | Xu, Yingqian Li, Xiaohan Wan, Caihua Zhang, Ran He, Bin Liu, Shiqiang Xia, Jihao Kong, Dehao Xiong, Shilong Yu, Guoqiang Han, Xiufeng |
| author_facet | Xu, Yingqian Li, Xiaohan Wan, Caihua Zhang, Ran He, Bin Liu, Shiqiang Xia, Jihao Kong, Dehao Xiong, Shilong Yu, Guoqiang Han, Xiufeng |
| contents | Bayesian networks play an increasingly important role in data mining, inference, and reasoning with the rapid development of artificial intelligence. In this paper, we present proof-of-concept experiments demonstrating the use of spin-orbit torque magnetic tunnel junctions (SOT-MTJs) in Bayesian network reasoning. Not only can the target probability distribution function (PDF) of a Bayesian network be precisely formulated by a conditional probability table as usual but also quantitatively parameterized by a probabilistic forward propagating neuron network. Moreover, the parameters of the network can also approach the optimum through a simple point-by point training algorithm, by leveraging which we do not need to memorize all historical data nor statistically summarize conditional probabilities behind them, significantly improving storage efficiency and economizing data pretreatment. Furthermore, we developed a simple medical diagnostic system using the SOT-MTJ as a random number generator and sampler, showcasing the application of SOT-MTJ-based Bayesian reasoning. This SOT-MTJ-based Bayesian reasoning shows great promise in the field of artificial probabilistic neural network, broadening the scope of spintronic device applications and providing an efficient and low-storage solution for complex reasoning tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08257 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian Reasoning Enabled by Spin-Orbit Torque Magnetic Tunnel Junctions Xu, Yingqian Li, Xiaohan Wan, Caihua Zhang, Ran He, Bin Liu, Shiqiang Xia, Jihao Kong, Dehao Xiong, Shilong Yu, Guoqiang Han, Xiufeng Applied Physics Artificial Intelligence Bayesian networks play an increasingly important role in data mining, inference, and reasoning with the rapid development of artificial intelligence. In this paper, we present proof-of-concept experiments demonstrating the use of spin-orbit torque magnetic tunnel junctions (SOT-MTJs) in Bayesian network reasoning. Not only can the target probability distribution function (PDF) of a Bayesian network be precisely formulated by a conditional probability table as usual but also quantitatively parameterized by a probabilistic forward propagating neuron network. Moreover, the parameters of the network can also approach the optimum through a simple point-by point training algorithm, by leveraging which we do not need to memorize all historical data nor statistically summarize conditional probabilities behind them, significantly improving storage efficiency and economizing data pretreatment. Furthermore, we developed a simple medical diagnostic system using the SOT-MTJ as a random number generator and sampler, showcasing the application of SOT-MTJ-based Bayesian reasoning. This SOT-MTJ-based Bayesian reasoning shows great promise in the field of artificial probabilistic neural network, broadening the scope of spintronic device applications and providing an efficient and low-storage solution for complex reasoning tasks. |
| title | Bayesian Reasoning Enabled by Spin-Orbit Torque Magnetic Tunnel Junctions |
| topic | Applied Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2504.08257 |