arXiv：cs.LG· Suresh Kumar TP, Vikas Agnihotri·· 13 小时前
神经形态卫星智能：事件驱动架构、脉冲算法与能耗-精度权衡的形式化框架
Neuromorphic Satellite Intelligence: A Formal Framework for Event-Driven Architectures, Spike-Based Algorithms, and Quantitative Energy-Accuracy Trade-offs
AI 导读
该论文为卫星星载计算提出脉冲神经网络的形式化数学模型，并给出三种原创算法：用于姿态估计的 SKCF、用于航天器健康监测的 TCAD 和用于认知卫星链路的 SAMC。基于 Loihi 与 SpiNNaker 已发表硅测量数据构建 Energy-Accuracy-Latency 权衡面，与 FPGA、GPU 基线对比显示神经形态实现可实现 8 至 47 倍能耗降低，推理精度损失在 2.1% 以内。
正文
Abstract:Satellite onboard computing faces an irreducible tension: mission intelligence requirements grow monotonically while power, mass, and radiation budgets remain fixed. This paper advances the state of the art on three fronts. First, we develop a formal mathematical model of spiking neural computation for satellite workloads, deriving closed-form expressions for energy consumption as a function of network sparsity, firing rate, and synaptic fan-out. Second, we propose three original algorithms -- a Spiking Kalman-Complementary Filter (SKCF) for attitude estimation, a Temporal Contrast Anomaly Detector (TCAD) for spacecraft health monitoring, and a Spike-Based Adaptive Modulation and Coding (SAMC) scheme for cognitive satellite links -- each with pseudocode and analytical complexity bounds. Third, we introduce a quantitative Energy-Accuracy-Latency (EAL) trade-off surface and populate it with numerical estimates derived from published silicon measurements of Loihi and SpiNNaker, enabling direct comparison with FPGA and GPU baselines. A taxonomy of four radiation-aware neuromorphic architecture classes is refined with fault-rate models. Continual on-orbit learning is formalized through a constrained Spike-Timing Dependent Plasticity (STDP) rule that provably bounds weight drift. Case studies for a 12U CubeSat Earth observation mission, a GEO communications payload, and an autonomous deep-space probe provide concrete, quantified design points. Our analysis shows that neuromorphic implementations can achieve 8 to 47 times energy reduction over GPU baselines while maintaining inference accuracy within 2.1\% for representative satellite workloads.
| Subjects: | Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.12471 [cs.NE] |
| (or arXiv:2610.12471v1 [cs.NE] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12471 arXiv-issued DOI via DataCite |
Submission history
From: Vikas Agnihotri [view email]
[v1]
Mon, 11 May 2026 12:35:37 UTC (401 KB)
来源：arXiv：cs.LG · arxiv.org