Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networks

• URL: http://arxiv.org/abs/2501.15995v1
• Date: Mon, 27 Jan 2025 12:29:47 GMT
• Title: Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networks
• Authors: Peng Yang, Ting Wang, Haibin Cai, Yuanming Shi, Chunxiao Jiang, Linling Kuang,
• Abstract summary: Space Computing Power Networks (Space-CPN) emerges as a promising architecture to coordinate the computing capability of satellites and enable on board data processing. We propose to employ spiking neural networks (SNNs), which is supported by the neuromorphic computing architecture, for on-board data processing. We put forward a decentralized neuromorphic learning framework, where a communication-efficient inter-plane model aggregation method is developed.
• Score: 42.67808523367945
• License:
• Abstract: 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.

Related papers

• Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
  Neuromorphic computing uses spiking neural networks (SNNs) to perform inference tasks. embedding a small payload within each spike exchanged between spiking neurons can enhance inference accuracy without increasing energy consumption. split computing - where an SNN is partitioned across two devices - is a promising solution. This paper presents the first comprehensive study of a neuromorphic wireless split computing architecture that employs multi-level SNNs.
  arXiv  Detail & Related papers  (2024-11-07T14:08:35Z)
• A Distance Similarity-based Genetic Optimization Algorithm for Satellite Ground Network Planning Considering Feeding Mode [53.71516191515285]
  The low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system. We propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks.
  arXiv  Detail & Related papers  (2024-08-29T06:57:45Z)
• Hierarchical Learning and Computing over Space-Ground Integrated Networks [40.19542938629252]
  We propose a hierarchical learning and computing framework to provide global aggregation services for locally trained models on ground IoT devices. We formulate a network energy problem for model aggregation, which turns out to be a Directed Steiner Tree (DST) problem. We propose a topologyaware energy-efficient routing (TAEER) algorithm to solve the DST problem by finding a minimum spanning arborescence on a substitute directed graph.
  arXiv  Detail & Related papers  (2024-08-26T09:05:43Z)
• Toward Autonomous Cooperation in Heterogeneous Nanosatellite Constellations Using Dynamic Graph Neural Networks [0.0]
  The paper proposes a novel approach to overcome the challenges by modeling the constellations and CP as dynamic networks. The trained neural network can predict the network delay with a mean absolute error of 3.6 minutes. Simulation results show that the proposed method can successfully design a contact plan for large satellite networks, improving the delay by 29.1%, similar to a traditional approach.
  arXiv  Detail & Related papers  (2024-03-01T17:26:02Z)
• Recent Advances in Scalable Energy-Efficient and Trustworthy Spiking Neural networks: from Algorithms to Technology [11.479629320025673]
  spiking neural networks (SNNs) have become an attractive alternative to deep neural networks for a broad range of signal processing applications. We describe advances in algorithmic and optimization innovations to efficiently train and scale low-latency, and energy-efficient SNNs. We discuss the potential path forward for research in building deployable SNN systems.
  arXiv  Detail & Related papers  (2023-12-02T19:47:00Z)
• Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing [59.40731173370976]
  We investigate the application of energy-efficient brain-inspired machine learning models for on-board radio resource management. For relevant workloads, spiking neural networks (SNNs) implemented on Loihi 2 yield higher accuracy, while reducing power consumption by more than 100$times$ as compared to the CNN-based reference platform.
  arXiv  Detail & Related papers  (2023-08-22T03:13:57Z)
• Olive Branch Learning: A Topology-Aware Federated Learning Framework for Space-Air-Ground Integrated Network [19.059950250921926]
  Training AI models centrally with the assistance of SAGIN faces the challenges of highly constrained network topology, inefficient data transmission, and privacy issues. We first propose a novel topology-aware federated learning framework for the SAGIN, namely Olive Branch Learning (OBL) We extend our OBL framework and CNASA algorithm to adapt to more complex multi-orbit satellite networks.
  arXiv  Detail & Related papers  (2022-12-02T14:51:42Z)
• Machine Learning-Based User Scheduling in Integrated Satellite-HAPS-Ground Networks [82.58968700765783]
  Integrated space-air-ground networks promise to offer a valuable solution space for empowering the sixth generation of communication networks (6G) This paper showcases the prospects of machine learning in the context of user scheduling in integrated space-air-ground communications.
  arXiv  Detail & Related papers  (2022-05-27T13:09:29Z)
• Communication-Efficient Separable Neural Network for Distributed Inference on Edge Devices [2.28438857884398]
  We propose a novel method of exploiting model parallelism to separate a neural network for distributed inferences. Under proper specifications of devices and configurations of models, our experiments show that the inference of large neural networks on edge clusters can be distributed and accelerated.
  arXiv  Detail & Related papers  (2021-11-03T19:30:28Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.