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.