Resonant-Tunnelling Diode Reservoir Computing System for Image Recognition

• URL: http://arxiv.org/abs/2507.15158v2
• Date: Fri, 25 Jul 2025 00:08:12 GMT
• Title: Resonant-Tunnelling Diode Reservoir Computing System for Image Recognition
• Authors: A. H. Abbas, Hend Abdel-Ghani, Ivan S. Maksymov,
• Abstract summary: 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)<n>Our results show that this circuit-level architecture delivers promising performance while adhering to the principles of next-generation RC.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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