A Cloud-Edge Framework for Energy-Efficient Spiking Neural Network-Based Control with Obstacle Avoidance Capabilities

Conceitos essenciais

The proposed cloud-edge framework employs an energy-efficient spiking neural network (SNN) to learn and reproduce control signals from a cloud-based controller, enabling reliable and autonomous control of a physical plant (e.g., satellite) with obstacle avoidance capabilities.

Resumo

The study presents a novel cloud-edge framework that integrates online supervised learning, spiking neural networks (SNNs), and local plasticity rules to address computational and energy constraints in complex control systems. The key aspects of the framework are:

The SNN, positioned on the physical plant (e.g., satellite), learns to reproduce control signals generated by a cloud-based controller using a biologically plausible supervised learning method with local plasticity rules. This reduces the need for constant plant-cloud communication.

The SNN updates its weights only when the tracking error exceeds a predefined threshold, ensuring efficiency and robustness under various conditions.

The event-driven nature of SNNs minimizes energy consumption, utilizing only about 11.1×104 pJ (0.3% of conventional computing requirements) for a linear workbench system.

The framework is applied to a satellite rendezvous scenario, including static and dynamic obstacle avoidance. The results demonstrate the SNN's ability to adjust to changing environments and efficiently utilize 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.

The SNN exhibits high responsiveness and precision in mimicking the control inputs, with rapid error convergence, making it suitable for dynamic and time-sensitive applications like satellite rendezvous.

The framework's versatility allows easy implementation on various plants with different control methods, as the SNN is independent of the underlying dynamics of the system and the controller.

Estatísticas

The SNN utilized only 1,444 spikes during the 10-second simulation for the linear workbench system, which is 4.81% of the potential 30,000 spikes (for 30 neurons across 1,000 time steps).
For the satellite rendezvous without obstacle, the SNN emitted 3,434 spikes across 50 LIF neurons, just 0.19% of the potential 1,800,000 spikes.
For the satellite rendezvous with a static obstacle, the SNN used 4,382 spikes, which constitutes only 0.24% of the total possible spikes.
The total energy consumption for the satellite rendezvous without obstacle was 8.1×104 pJ.
The total energy consumption for the satellite rendezvous with a static obstacle was 8.1×104 pJ by t=200s, and then remained relatively stable.

Citações

"The event-driven nature of SNNs minimizes energy consumption, utilizing only about 11.1×104 pJ (0.3% of conventional computing requirements) for a linear workbench system."
"The SNN exhibits high responsiveness and precision in mimicking the control inputs, with rapid error convergence, making it suitable for dynamic and time-sensitive applications like satellite rendezvous."
"The framework's versatility allows easy implementation on various plants with different control methods, as the SNN is independent of the underlying dynamics of the system and the controller."

Principais Insights Extraídos De

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

by Reza Ahmadva... às arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.02316.pdf

Perguntas Mais Profundas

How can the proposed cloud-edge framework be extended to handle more complex scenarios, such as multiple obstacles or dynamic environments with uncertain parameters

The proposed cloud-edge framework can be extended to handle more complex scenarios by incorporating advanced algorithms and techniques. To address scenarios with multiple obstacles, the framework can be enhanced with sophisticated obstacle detection and avoidance strategies. This can involve integrating sensor data to identify obstacles in the environment and adjusting the control signals generated by the SNN to navigate around them effectively. Additionally, the framework can be augmented with reinforcement learning algorithms to enable the system to learn and adapt to new obstacles dynamically.
In dynamic environments with uncertain parameters, the framework can leverage probabilistic modeling and uncertainty quantification techniques. By incorporating probabilistic models, the system can account for uncertainties in the environment and make decisions based on probabilistic outcomes. This can involve using Bayesian inference methods to update beliefs about the environment and adjust control strategies accordingly. Furthermore, the framework can integrate adaptive control techniques to continuously learn and optimize control policies in response to changing environmental conditions.

What are the potential challenges and limitations in deploying the SNN-based control system on actual hardware platforms, and how can they be addressed

Deploying the SNN-based control system on actual hardware platforms may pose several challenges and limitations. One potential challenge is the hardware constraints in terms of computational resources and memory capacity. SNNs can be computationally intensive, requiring specialized hardware accelerators or neuromorphic chips for efficient implementation. Addressing this challenge involves optimizing the network architecture and developing hardware platforms tailored for SNN computations.
Another challenge is the training and tuning of the SNN parameters. Training SNNs can be complex and time-consuming, requiring large datasets and computational resources. To address this challenge, techniques such as transfer learning and online learning can be employed to facilitate faster and more efficient training of the SNN. Additionally, developing automated tuning algorithms can help optimize the network parameters for specific control tasks.
Furthermore, the interpretability and explainability of SNN-based control systems can be a limitation. Understanding the decision-making process of SNNs and interpreting their behavior can be challenging, especially in safety-critical applications. Addressing this limitation involves developing explainable AI techniques to provide insights into the SNN's decision-making process and ensure transparency in its actions.

What other applications, beyond satellite rendezvous, could benefit from the energy-efficient and adaptive control capabilities of the proposed framework

The energy-efficient and adaptive control capabilities of the proposed framework can benefit various applications beyond satellite rendezvous. Some potential applications include autonomous vehicles, robotics, and smart grid systems.
In autonomous vehicles, the framework can be utilized for real-time decision-making and control, enabling vehicles to navigate complex environments efficiently while conserving energy. The adaptive nature of the SNN-based control system can help vehicles respond to dynamic traffic conditions and unexpected obstacles, enhancing safety and performance.
In robotics, the framework can be applied to autonomous robots for tasks such as object manipulation, path planning, and obstacle avoidance. The energy-efficient operation of the SNNs can prolong the robot's battery life and enable continuous operation in resource-constrained environments. The adaptive control capabilities can also enhance the robot's ability to adapt to changing tasks and environments.
In smart grid systems, the framework can be used for optimizing energy consumption, managing renewable energy sources, and enhancing grid stability. By integrating the SNN-based control system, smart grids can dynamically adjust energy generation and distribution based on real-time data, leading to improved efficiency and reliability in energy management.