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用于图像识别的谐波隧道二极管水库计算系统

Resonant-Tunnelling Diode Reservoir Computing System for Image Recognition

A. H. Abbas, Hend Abdel-Ghani, and Ivan S. Maksymov

arXiv
2025年7月20日

随着人工智能继续推进实时、基于边缘和资源受限的环境,迫切需要新颖、硬件高效的计算模型。 在这项研究中,我们展示并验证了基于谐成调谐二极管(RTD)的神经形态计算架构,该架构表现出物理储层计算(RC)的理想非线性特征。 我们理论上制定和定量地实施基于RTD的RC系统,并在两个图像识别基准上展示其有效性:手写数字分类和使用Fruit 360数据集的对象识别。 我们的结果表明,这种电路级架构在坚持下一代RC原则的同时提供有希望的性能 - 消除随机连接,有利于输入信号的确定性非线性转换。

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 effecti...