Self-organizing Architecture of Receptron Units: a Hardware-Aware Framework for Edge Intelligence
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 baseli...
| Authors | Stefano Radice, Ludovico Casaccia, Riccaro Emanuele Beccalli et al. |
|---|---|
| Primary category | cs.LG |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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