# Self-organizing Architecture of Receptron Units: a Hardware-Aware Framework for Edge Intelligence

arXiv: https://arxiv.org/abs/2607.20162 · HTML: https://mlsanity.com/paper/2607.20162/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 22 Jul 2026.

## Builds on (most cited first, 1 of 1)

- [AI for Next Generation Computing: Emerging Trends and Future Directions](https://mlsanity.com/paper/2203.04159/) (2022)

## Abstract (arXiv)

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.