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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.03260 |
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| _version_ | 1866914902772285440 |
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| author | Singh, Chandan Ntinas, Vasileios Prousalis, Dimitrios Wang, Yongmin Demirkol, Ahmet Samil Messaris, Ioannis Rana, Vikas Menzel, Stephan Ascoli, Alon Tetzlaff, Ronald |
| author_facet | Singh, Chandan Ntinas, Vasileios Prousalis, Dimitrios Wang, Yongmin Demirkol, Ahmet Samil Messaris, Ioannis Rana, Vikas Menzel, Stephan Ascoli, Alon Tetzlaff, Ronald |
| contents | This paper introduces an innovative graphical analysis tool for investigating the dynamics of Memristor Cellular Nonlinear Networks (M-CNNs) featuring 2nd-order processing elements, known as M-CNN cells. In the era of specialized hardware catering to the demands of intelligent autonomous systems, the integration of memristors within Cellular Nonlinear Networks (CNNs) has emerged as a promising paradigm due to their exceptional characteristics. However, the standard Dynamic Route Map (DRM) analysis, applicable to 1st-order systems, fails to address the intricacies of 2nd-order M-CNN cell dynamics, as well the 2nd-order DRM (DRM2) exhibits limitations on the graphical illustration of local dynamical properties of the M-CNN cells, e.g. state derivative's magnitude. To address this limitation, we propose a novel integration of M-CNN cell vector field into the cell's phase portrait, enhancing the analysis efficacy and enabling efficient M-CNN cell design. A comprehensive exploration of M-CNN cell dynamics is presented, showcasing the utility of the proposed graphical tool for various scenarios, including bistable and monostable behavior, and demonstrating its superior ability to reveal subtle variations in cell behavior. Through this work, we offer a refined perspective on the analysis and design of M-CNNs, paving the way for advanced applications in edge computing and specialized hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_03260 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Employing Vector Field Techniques on the Analysis of Memristor Cellular Nonlinear Networks Cell Dynamics Singh, Chandan Ntinas, Vasileios Prousalis, Dimitrios Wang, Yongmin Demirkol, Ahmet Samil Messaris, Ioannis Rana, Vikas Menzel, Stephan Ascoli, Alon Tetzlaff, Ronald Emerging Technologies This paper introduces an innovative graphical analysis tool for investigating the dynamics of Memristor Cellular Nonlinear Networks (M-CNNs) featuring 2nd-order processing elements, known as M-CNN cells. In the era of specialized hardware catering to the demands of intelligent autonomous systems, the integration of memristors within Cellular Nonlinear Networks (CNNs) has emerged as a promising paradigm due to their exceptional characteristics. However, the standard Dynamic Route Map (DRM) analysis, applicable to 1st-order systems, fails to address the intricacies of 2nd-order M-CNN cell dynamics, as well the 2nd-order DRM (DRM2) exhibits limitations on the graphical illustration of local dynamical properties of the M-CNN cells, e.g. state derivative's magnitude. To address this limitation, we propose a novel integration of M-CNN cell vector field into the cell's phase portrait, enhancing the analysis efficacy and enabling efficient M-CNN cell design. A comprehensive exploration of M-CNN cell dynamics is presented, showcasing the utility of the proposed graphical tool for various scenarios, including bistable and monostable behavior, and demonstrating its superior ability to reveal subtle variations in cell behavior. Through this work, we offer a refined perspective on the analysis and design of M-CNNs, paving the way for advanced applications in edge computing and specialized hardware. |
| title | Employing Vector Field Techniques on the Analysis of Memristor Cellular Nonlinear Networks Cell Dynamics |
| topic | Emerging Technologies |
| url | https://arxiv.org/abs/2408.03260 |