Neuromorphic Satellite Intelligence: A Formal Framework for Event-Driven Architectures, Spike-Based Algorithms, and Quantitative Energy-Accuracy Trade-offs
This work introduces a formal mathematical model for spiking neural networks in satellite applications to achieve massive energy savings over GPUs while maintaining high accuracy. It proposes three original algorithms—SKCF, TCAD, and SAMC—and defines four radiation-aware architecture classes. The findings demonstrate 8–47x energy reduction with only a 2.1% drop in inference accuracy for satellite tasks.