Edge-Embedded System-on-Chip Architecture for Unified Transformer-Based AI in Cross-Domain Energy Systems

Recent advances in deep learning hardware often fail to deliver crossdomain portability, multimodal signal processing, and task-adaptive inference essential for smart grids, UAVs, and spacecraft systems. This paper introduces a novel System-onChip (SoC) design tailored for the UCMTransformer—a unified Transformer-GNN hybrid model capable of realtime forecasting, control, and fault detection across Earth and aerospace domains. Our design incorporates neuromorphic processors, compute-inmemory accelerators, and graph-aware dataflow to bridge gaps found in 20 state-of-the-art IEEE SoC publications. We validate our architecture through simulation and embedded deployment benchmarks.