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June 30, 2026Open Access

Dismantling the GPU-HBM Dependency Loop: Stateless O(1) Memory Virtualization via J.M. Resonance for AI Infrastructure Downsizing

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Authors

MJMin Ho JungKorea Soongsil Cyber ​​University

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Overview

Randomized trial explores stateless memory virtualization in AI infrastructure, suggesting significant downsizing opportunities.

Key Points

  • This research aims to disrupt the traditional GPU-HBM dependency in AI data infrastructures by introducing stateless memory virtualization.
  • Introduced the virtual Quantum Processing Unit (vQPU) to bypass traditional memory limitations.
  • Utilized the J.M. Resonance Function for high-dimensional weight mapping in a 9,192-dimensional lattice domain.
  • Implemented Adiabatic Charge-Recovery Logic (ACRL) under a coupled Hamiltonian framework.
  • Achieved deterministic O(1) spatiotemporal reconstruction within 0.46 ms using a 64-byte coordinate seed.
  • Reduced active core power consumption to 23.9 µW by bypassing Landauer's limit.
  • Enabled zero-bandwidth AI inference on standard edge devices, eliminating the need for expensive HBM packages.

Cite This Study

Min Ho Jung (2026) studied this question.

synapsesocial.com/papers/6a435c65759b888809a52deehttps://doi.org/10.5281/zenodo.20983588
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