Authors : Adnan Haider Zaidi

Volume/Issue : Volume 10 - 2025, Issue 6 - June

Google Scholar : https://tinyurl.com/yckfxy8a

DOI : https://doi.org/10.38124/ijisrt/25jun1314

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This paper presents a novel SoC architecture tailored for implementing Transformer-GNN-based AI models across domains such as Earth-based smart grids, spacecraft, UAVs, and commercial aviation. The proposed chip integrates recent hardware design strategies including In-Memory Computing (IMC) [3], Neuromorphic Coprocessing [5], and NoC-based modularity [8] to address latency, power, and domain adaptation challenges. Our contribution fills hardware-software integration gaps identified in 20 IEEE chip design papers and introduces a patentable blueprint for unified edge-AI deployment [1]–[20]. System-on-Chip (SoC), Transformer, Graph Neural Network (GNN), Smart Grid, Spacecraft AI, Neuromorphic Coprocessor, In-Memory Computing, CrossDomain AI.

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