# A Cloud-Edge Framework for Energy-Efficient Event-Driven Control: An Integration of Online Supervised Learning, Spiking Neural Networks and Local Plasticity Rules

arXiv: https://arxiv.org/abs/2405.02316 · HTML: https://mlsanity.com/paper/2405.02316/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 12 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [An Analytical Estimation of Spiking Neural Networks Energy Efficiency](https://mlsanity.com/paper/2210.13107/) (2022)
- [Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network](https://mlsanity.com/paper/1702.06463/) (2017)
- [Closed-form control with spike coding networks](https://mlsanity.com/paper/2212.12887/) (2022)
- [Enhancing Energy Efficiency and Reliability in Autonomous Systems Estimation using Neuromorphic Approach](https://mlsanity.com/paper/2307.07963/) (2023)
- [Neuromorphic Robust Framework for Concurrent Estimation and Control in Dynamical Systems using Spiking Neural Networks](https://mlsanity.com/paper/2310.03873/) (2023)

## Built on by (most cited first, 1 of 1)

- [Brain-inspired spike-timing plasticity for reliable label-efficient event-camera vision](https://mlsanity.com/paper/2605.17686/) (2026)

## Abstract (arXiv)

This paper presents a novel cloud-edge framework for addressing computational and energy constraints in complex control systems. Our approach centers around a learning-based controller using Spiking Neural Networks (SNN) on physical plants. By integrating a biologically plausible learning method with local plasticity rules, we harness the efficiency, scalability, and low latency of SNNs. This design replicates control signals from a cloud-based controller directly on the plant, reducing the need for constant plant-cloud communication. The plant updates weights only when errors surpass predefined thresholds, ensuring efficiency and robustness in various conditions. Applied to linear workbench systems and satellite rendezvous scenarios, including obstacle avoidance, our architecture dramatically lowers normalized tracking error by 96% with increased network size. The event-driven nature of SNNs minimizes energy consumption, utilizing only about 111 nJ (0.3% of conventional computing requirements). The results demonstrate the system's adjustment to changing work environments and its efficient use of computational and energy resources, with a moderate increase in energy consumption of 27.2% and 37% for static and dynamic obstacles, respectively, compared to non-obstacle scenarios.