{"ID":2892713,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.15158","arxiv_id":"2507.15158","title":"Resonant-Tunnelling Diode Reservoir Computing System for Image Recognition","abstract":"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.","short_abstract":"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 exhibi...","url_abs":"https://arxiv.org/abs/2507.15158","url_pdf":"https://arxiv.org/pdf/2507.15158v2","authors":"[\"A. H. Abbas\",\"Hend Abdel-Ghani\",\"Ivan S. Maksymov\"]","published":"2025-07-20T23:50:32Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"physics.app-ph\"]","methods":"[]","has_code":false}