Analysis on reservoir activation with the nonlinearity harnessed from solution-processed molybdenum disulfide
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911215485190144 |
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| author | Liu, Songwei Wen, Yingyi Pei, Jingfang Liu, Yang Song, Lekai Liu, Pengyu Fan, Xiaoyue Yang, Wenchen Pan, Danmei Ma, Teng Lin, Yue Wang, Gang Hu, Guohua |
| author_facet | Liu, Songwei Wen, Yingyi Pei, Jingfang Liu, Yang Song, Lekai Liu, Pengyu Fan, Xiaoyue Yang, Wenchen Pan, Danmei Ma, Teng Lin, Yue Wang, Gang Hu, Guohua |
| contents | Reservoir computing is a recurrent neural network designed for approximating complex dynamics in, for instance, motion tracking, spatial-temporal pattern recognition, and chaotic attractor reconstruction. Its implementation demands intense computation for the nonlinear transformation of the reservoir input, i.e. activating the reservoir. Configuring physical nonlinear networks as the reservoir and employing the physical nonlinearity for the reservoir activation is an emergent solution to address the challenge. In this work, we analyze the feasibility of harnessing the nonlinearity from solution-processed molybdenum disulfide (MoS2) for reservoir activation. We fit the high-order nonlinearity, achieved by Stark modulation of MoS2, as the activation function to facilitate implementation of a reservoir computing model. Due to the high-order nonlinearity, the model can achieve long-term synchronization and robust generalization for complex dynamical system regression. As a potential application exploring this ability, we appoint the model to generate chaotic random numbers for secure data encryption. Given this reservoir activation capability, and the scalability of solution-processed MoS2, our results suggest the potential for realizing physical reservoir computing with solution-processed MoS2. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17676 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Analysis on reservoir activation with the nonlinearity harnessed from solution-processed molybdenum disulfide Liu, Songwei Wen, Yingyi Pei, Jingfang Liu, Yang Song, Lekai Liu, Pengyu Fan, Xiaoyue Yang, Wenchen Pan, Danmei Ma, Teng Lin, Yue Wang, Gang Hu, Guohua Applied Physics Emerging Technologies Reservoir computing is a recurrent neural network designed for approximating complex dynamics in, for instance, motion tracking, spatial-temporal pattern recognition, and chaotic attractor reconstruction. Its implementation demands intense computation for the nonlinear transformation of the reservoir input, i.e. activating the reservoir. Configuring physical nonlinear networks as the reservoir and employing the physical nonlinearity for the reservoir activation is an emergent solution to address the challenge. In this work, we analyze the feasibility of harnessing the nonlinearity from solution-processed molybdenum disulfide (MoS2) for reservoir activation. We fit the high-order nonlinearity, achieved by Stark modulation of MoS2, as the activation function to facilitate implementation of a reservoir computing model. Due to the high-order nonlinearity, the model can achieve long-term synchronization and robust generalization for complex dynamical system regression. As a potential application exploring this ability, we appoint the model to generate chaotic random numbers for secure data encryption. Given this reservoir activation capability, and the scalability of solution-processed MoS2, our results suggest the potential for realizing physical reservoir computing with solution-processed MoS2. |
| title | Analysis on reservoir activation with the nonlinearity harnessed from solution-processed molybdenum disulfide |
| topic | Applied Physics Emerging Technologies |
| url | https://arxiv.org/abs/2403.17676 |