标题： 基于脑启发的太空计算能力网络中的去中心化卫星学习 显示英文标题

标题： Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networks

摘要： 卫星网络能够利用先进的遥感技术收集大量空间信息，这对于自然灾害监测等实时应用至关重要。 然而，传统的地面服务器集中式处理会由于原始数据传输瓶颈导致严重的时效性问题。 为此，空间计算供电网络（Space-CPN）作为一种有前景的架构应运而生，它能够协调卫星的计算能力并实现机载数据处理。 然而，由于太阳能板的自然限制，卫星电源系统难以满足人工智能神经网络日益增长的智能计算任务的能源需求。 为了解决这个问题，我们提出采用脉冲神经网络（SNNs），该网络由类脑计算架构支持，用于机载数据处理。 其计算中的极端稀疏性使其具有高能效。 此外，为了实现这些机载模型的有效训练，我们提出了一种去中心化的类脑学习框架，其中受RelaySum的启发，开发了一种通信高效的平面间模型聚合方法。 我们提供了理论分析来描述所提出算法的收敛行为，该分析揭示了与网络直径相关的收敛速度。 随后，我们在平面间连接拓扑上制定了最小直径生成树问题，并对其进行求解以进一步提高学习性能。 进行了大量实验，以评估所提出方法相对于基准方法的优势。 显示英文摘要

摘要： Satellite networks are able to collect massive space information with advanced remote sensing technologies, which is essential for real-time applications such as natural disaster monitoring. However, traditional centralized processing by the ground server incurs a severe timeliness issue caused by the transmission bottleneck of raw data. To this end, Space Computing Power Networks (Space-CPN) emerges as a promising architecture to coordinate the computing capability of satellites and enable on board data processing. Nevertheless, due to the natural limitations of solar panels, satellite power system is difficult to meet the energy requirements for ever-increasing intelligent computation tasks of artificial neural networks. To tackle this issue, we propose to employ spiking neural networks (SNNs), which is supported by the neuromorphic computing architecture, for on-board data processing. The extreme sparsity in its computation enables a high energy efficiency. Furthermore, to achieve effective training of these on-board models, we put forward a decentralized neuromorphic learning framework, where a communication-efficient inter-plane model aggregation method is developed with the inspiration from RelaySum. We provide a theoretical analysis to characterize the convergence behavior of the proposed algorithm, which reveals a network diameter related convergence speed. We then formulate a minimum diameter spanning tree problem on the inter-plane connectivity topology and solve it to further improve the learning performance. Extensive experiments are conducted to evaluate the superiority of the proposed method over benchmarks.