Resonant-Tunnelling Diode Reservoir Computing System for Image Recognition
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918104121999360 |
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| author | Abbas, A. H. Abdel-Ghani, Hend Maksymov, Ivan S. |
| author_facet | Abbas, A. H. Abdel-Ghani, Hend Maksymov, Ivan S. |
| contents | As artificial intelligence continues to push into real-time, edge-based and resource-constrained environments, there is an urgent need for novel, hardware-efficient computational models. In this study, we present and validate a neuromorphic computing architecture based on resonant-tunnelling diodes (RTDs), which exhibit the nonlinear characteristics ideal for physical reservoir computing (RC). We theoretically formulate and numerically implement an RTD-based RC system and demonstrate its effectiveness on two image recognition benchmarks: handwritten digit classification and object recognition using the Fruit~360 dataset. Our results show that this circuit-level architecture delivers promising performance while adhering to the principles of next-generation RC -- eliminating random connectivity in favour of a deterministic nonlinear transformation of input signals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15158 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Resonant-Tunnelling Diode Reservoir Computing System for Image Recognition Abbas, A. H. Abdel-Ghani, Hend Maksymov, Ivan S. Machine Learning Applied Physics As artificial intelligence continues to push into real-time, edge-based and resource-constrained environments, there is an urgent need for novel, hardware-efficient computational models. In this study, we present and validate a neuromorphic computing architecture based on resonant-tunnelling diodes (RTDs), which exhibit the nonlinear characteristics ideal for physical reservoir computing (RC). We theoretically formulate and numerically implement an RTD-based RC system and demonstrate its effectiveness on two image recognition benchmarks: handwritten digit classification and object recognition using the Fruit~360 dataset. Our results show that this circuit-level architecture delivers promising performance while adhering to the principles of next-generation RC -- eliminating random connectivity in favour of a deterministic nonlinear transformation of input signals. |
| title | Resonant-Tunnelling Diode Reservoir Computing System for Image Recognition |
| topic | Machine Learning Applied Physics |
| url | https://arxiv.org/abs/2507.15158 |