Neuromorphic Satellite Intelligence: A Formal Framework for Event-Driven Architectures, Spike-Based Algorithms, and Quantitative Energy-Accuracy Trade-offs
Authors: Suresh Kumar TP, Vikas Agnihotri
Organizations: Department of Electrical and Electronics Engineering, Mangalayatan University, India · Independent Researcher, India
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.
TABLE II: Estimated energy comparison: Neuromorphic figures from Eq. (4) with 180nm rad-hard parameters; FPGA/GPU from published benchmarks
Class
Rad. Strategy
Hybridization
Topology
Learning
Target Mission
Rad-Neu
TMR + rad-hard SRAM
None (pure SNN)
Monolithic
Offline only
Deep space
NeuHybrid
Partial TMR + SEFI scrubbing
SNN + FPGA/CPU
Monolithic
Supervised fine-tune
LEO/MEO
DEC-Node
Redundant spike paths
SNN + comms ASIC
Distributed constellation
Federated STDP
Constellation
NeuroSoC
Process-level hardening
SNN + DSP + RISC-V
Monolithic SoC
Full on-orbit STDP
GEO
TABLE III: Proposed neuromorphic architecture taxonomy for satellite systems
Fig. 1: Energy-Accuracy-Latency (EAL) Trade-off Analysis for Neuromorphic Satellite Computing.
Design Point
N (neurons)
r (Hz)
Accuracy (%)
Energy ( μ J)
Latency (ms)
Minimal power
2,000
0.5
91.2
0.8
7.2
Balanced
16,000
2.0
97.8
18.3
4.1
High accuracy
64,000
5.0
99.3
154
1.8
FPGA baseline
N/A
N/A
99.5
18,400
0.9
GPU baseline
N/A
N/A
99.6
142,000
0.3
TABLE IV: EAL trade-off design points for attitude estimation (SKCF algorithm). Neuromorphic estimates from Eq. (4); baselines from published benchmarks.
Low-earth-orbit (LEO) satellites enable high-resolution, large-scale Earth observation for applications such as disaster monitoring and environmental surveillance. However, cloud coverage often obscures the Earth's surface, and conventional cloud-removal pipelines that download cloudy images to ground stations for processing suffer from limited contact windows, constrained satellite-to-ground bandwidth, and high latency. In this work, we propose a novel satellite federated learning framework for cloud removal across LEO constellations, named orbital attention leaky integrate-and-fire (OrbitALIF). OrbitALIF performs both onboard training and inference using a compact 2.30,M-parameter spiking neural network (SNN) backbone with an adaptive gated fusion module (AGFM) and a spectral-spatial hybrid attention module (SHAM), combined with a decentralized federated learning strategy that shares model weights via inter-satellite links. Our experiments show that OrbitALIF achieves competitive cloud removal quality while consuming only 0.287,mJ per inference on neuromorphic hardware, a 72.3 times (98.6%) energy reduction versus an equivalent artificial neural network (ANN).
Bohan Zhang, Chenyu Xu, Yijie Mao +1
School of Information Science and Technology, ShanghaiTech University, Shanghai, China
We present the Real-Time Neuromorphic Spectrum Intelligence Simulator (RT-NuSIS), a modular framework to study spiking neural network (SNN) and memristor-inspired agents for dynamic spectrum access under constrained energy budgets and adversarial conditions. RT-NuSIS couples leaky integrate-and-fire neuronal dynamics, memristive synaptic models, physics-informed energy-harvesting models (triboelectric and RF), and adversary models including jamming and Byzantine behavior. We formalize the simulator mathematically, prove boundedness, present a mean-field adversary threshold, analyze per-step complexity, and provide a reproducible benchmark harness for energy-per-inference, latency, and robustness metrics. The codebase is modular, deterministic by seed, and designed for large-scale event-driven simulations.
Spiking neural networks (SNNs) are promoted as an energy-efficient substrate because sparse, event-driven activity replaces dense multiply-accumulates with cheap accumulates. We argue the energy dividend of sparsity is not a property of SNNs but of the task. Holding architecture fixed and swapping only the hidden unit (continuous vs. leaky-integrate-and-fire), plus a two-sided target-firing-rate probe, we measure how far activity can be pushed down before quality breaks. Low-load feed-forward perception sparsifies to 5% firing at no accuracy cost; a recurrent language model cannot go below ~50% -- the recurrent state must stay active to carry information. A spiking Transformer, by contrast, sparsifies freely to 2% (3 seeds) -- so the ceiling is a property of recurrent compression, not sequence modeling. Attention escapes the floor only by storing the full key-value cache, trading a firing floor for a memory wall: on neuromorphic hardware, recurrence and attention pay on different axes, neither escapes. We formalize the ceiling with an information-theoretic bound rho >= H_b^{-1}(log2 M / H) and confirm its predictions: the floor rises with memory load, falls with state width, and (refuting a naive memory-only reading) rises with task difficulty. A layer-wise input floor further caps op reduction under dense input, isolating event-driven perception as where neuromorphic hardware wins.