Receptron模型让边缘设备用单单元实现复杂分类，适合资源受限场景。
Self-organizing Architecture of Receptron Units: a Hardware-Aware Framework for Edge Intelligence
- 基于类脑思想的单单元分类器，无需多层网络即可处理非线性问题
- 在主流MCU上实现，支持设备端持续自适应，准确率接近传统机器学习基准
- 适合动态环境下的低功耗边缘智能系统，可解释性强
物联网边缘智能对计算与内存的需求日益增长，但微控制器（MCU）资源严重受限，使传统深度学习方法难以应用。本文提出一种受类脑启发的分类器——Receptron模型，该模型为单单元架构，可在不使用多层网络的情况下实现非线性可分决策边界。模型专为中等性能MCU设计，支持设备端持续自适应。在基础数据集基准上的实验表明，其交叉验证准确率与标准机器学习方法基线相当。结果表明，Receptron是资源受限类脑边缘系统在动态、非平稳环境中运行的一种可行且可解释的替代方案。
原文摘要 · Abstract (English)
The growing demand for intelligent processing at the edge of IoT networks is constrained by the severe computational and memory limitations of microcontroller units, which render impractical conventional deep learning approaches. We propose a neuromorphicinspired classifier based on the Receptron model, a single-unit architecture capable of implementing non-linearly separable decision boundaries, without resorting to multi-layer networks. The model is designed for direct deployment on mid-range MCUs, while supporting continuous on-device adaptation. Experimental evaluation on basic dataset benchmarks yields cross-validated accuracies compatible with standard machine learning method baselines. These results position the Receptron as a viable and interpretable alternative for resource-constrained neuromorphic edge systems operating in dynamic, non-stationary environments.
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