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What if a high-capacity, massively parallel non‑volatile storage substrate built upon static skyrmion lattices could feed enormous weight snapshots and continuous training checkpoints for large‑scale AI systems, without the bandwidth and latency bottlenecks that plague conventional storage hierarchies? The GUS architecture is a standalone pure storage framework engineered to serve as the dedicated persistent memory tier for the Wavon field‑based generalized in‑memory computing stack. It delivers stable topological long‑term archiving and near‑field ultra‑fast register resources tailored for post‑silicon AI hardware, where all interlayer signal coupling relies on 20–80 GHz microwave evanescent eigenstates shaped by static magnetic skyrmion boundary conditions. The system adopts three‑dimensional stacked nanostructures bounded strictly by subwavelength near‑field geometry. The lattice spacing 𝐷, effective action radius 𝑅 of the static skyrmion electromagnetic evanescent mode, and vertical interlayer near‑field gap ℎ satisfy ℎ ≤ 𝑅 < 𝐷/2 and ℎ ≤ 𝐷/3. MEPS, the Microwave Evanescent Pattern of Skyrmion, represents the coupled eigenstate ensemble formed by the magnetic topological lattice and its full induced evanescent field spectrum, distinguishable from isolated magnetic skyrmions, optical skyrmions and exciton skyrmions. A top‑layer nanogrid differential GWD wavefield detector enables contactless, non‑destructive, globally parallel near‑field sampling and electrical readout, capable of streaming immense volumes of model parameters simultaneously. Write, retention and read pathways are fully decoupled at the physical layer. The fabrication pipeline is compatible with the 28 nm ultraviolet nanoimprint co‑integration and vacuum microcavity stacking process shared with Wavon. GUS is purpose‑built to host giant transformer weight banks, iterative training checkpoints and fixed topological axiom parameters. It segregates low‑speed topological rewrite events from high‑speed field computation, removing the classic storage wall and forming a matched two‑component hardware ecosystem designed for sustained, large‑capacity field‑state generalized in‑memory computing.
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Xn.C (2026) studied this question.
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