用隧穿二极管实现高效图像识别的类脑计算系统
Resonant-Tunnelling Diode Reservoir Computing System for Image Recognition
- 基于共振隧穿二极管构建确定性非线性映射电路
- 在手写数字和水果分类任务中表现良好
- 适合边缘计算与低功耗硬件部署
随着人工智能向实时、边缘化和资源受限环境发展，亟需新型高效硬件计算模型。本研究提出并验证了一种基于共振隧穿二极管（RTD）的类脑计算架构，其非线性特性适用于物理储层计算（RC）。我们理论构建并数值实现了一个基于RTD的储层计算系统，在两个图像识别基准任务上进行验证：手写数字分类和使用Fruit360数据集的物体识别。结果表明，该电路级架构在遵循下一代储层计算原则的同时，实现了良好性能——以确定性非线性变换取代随机连接，显著提升能效与可扩展性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。